Academia.eduAcademia.edu

Figure 73 – uploaded by francisco revuelta

See full PDFdownloadDownload figure
A measure of the quality of the fit of the linear estimate to the data is the mean squared error (MSE) or the root mean squared error (RMSE) Observing the plot of these data points shown in Figure 10.1, it can be seen that a good estimate of a line passing through the points is 7 = 20x. The script used to generate this plot: where N is the number of measured data values (xz, y,), with estimated values %, = 20x;,. Note that the units of MSE are the square of the units of the measured quantity y,. The units of RMSE are the same as those of the measured quantity.

Figure 10 A measure of the quality of the fit of the linear estimate to the data is the mean squared error (MSE) or the root mean squared error (RMSE) Observing the plot of these data points shown in Figure 10.1, it can be seen that a good estimate of a line passing through the points is 7 = 20x. The script used to generate this plot: where N is the number of measured data values (xz, y,), with estimated values %, = 20x;,. Note that the units of MSE are the square of the units of the measured quantity y,. The units of RMSE are the same as those of the measured quantity.

Related Figures (114)

Figure 1.1: Initial velocity (v) divided into horizonal (vy) and vertical (v,) components. The key step in developing a mathematical model is to divide the trajectory into its horizontal and vertical components. The initial velocity can be divided in this way, as shown in Figure 1.1. From basic trigonometry, we know that
Figure 1.1: Initial velocity (v) divided into horizonal (vy) and vertical (v,) components. The key step in developing a mathematical model is to divide the trajectory into its horizontal and vertical components. The initial velocity can be divided in this way, as shown in Figure 1.1. From basic trigonometry, we know that
Figure 1.2: MATLAB generated plot of flight trajectory
Figure 1.2: MATLAB generated plot of flight trajectory
Figure 1.3: High-level language program development
Figure 1.3: High-level language program development
Figure 4.1: Estimating Building Height Problem: Consider the problem of estimating the height of a building, as illustrated in Fig. 4.1 If the observer is a distance D from the building, the angle from the observer to the top of the building is 6, and the height of the observer is h, what is the building height? Solution: Draw a simple diagram as shown in Fig. 4.1. The building height B is
Figure 4.1: Estimating Building Height Problem: Consider the problem of estimating the height of a building, as illustrated in Fig. 4.1 If the observer is a distance D from the building, the angle from the observer to the top of the building is 6, and the height of the observer is h, what is the building height? Solution: Draw a simple diagram as shown in Fig. 4.1. The building height B is
Figure 4.2: Distance and angle measurements near a lake In Figure 4.2, measurements of distances and angles around a lake have been indicated. The problem is to use these measurements to calculate the distance DE across the lake.
Figure 4.2: Distance and angle measurements near a lake In Figure 4.2, measurements of distances and angles around a lake have been indicated. The problem is to use these measurements to calculate the distance DE across the lake.
Included angles: a + 6+ = 7 radians = 180°
Included angles: a + 6+ = 7 radians = 180°
Figure 4.3: The complex number z in the complex plane This form of representation for complex numbers is called the rectangular or cartesian form since z can be represented in rectangular coordinates by the point (x,y) in a plane having a horizontal axis being the “real axis” and the vertical axis being the “imaginary axis,” as shown in Figure 4.3. This plane is called the “complex plane.” In MATLAB, i and j are variable names that default to the imaginary number. You have to be careful with their use, however, as they can be overridden and used as general variables. You can insure that j is the imaginary number by explicitly computing it as the square root of —1:
Figure 4.3: The complex number z in the complex plane This form of representation for complex numbers is called the rectangular or cartesian form since z can be represented in rectangular coordinates by the point (x,y) in a plane having a horizontal axis being the “real axis” and the vertical axis being the “imaginary axis,” as shown in Figure 4.3. This plane is called the “complex plane.” In MATLAB, i and j are variable names that default to the imaginary number. You have to be careful with their use, however, as they can be overridden and used as general variables. You can insure that j is the imaginary number by explicitly computing it as the square root of —1:
Figure 4.4: Complex number z = e/° in the complex plane For this case, z lies on on a circle of radius 1 centered on the origin of the complex plane, at angle 9, as shown in Figure 4.4.
Figure 4.4: Complex number z = e/° in the complex plane For this case, z lies on on a circle of radius 1 centered on the origin of the complex plane, at angle 9, as shown in Figure 4.4.
Figure 4.5: Addition and subtraction of complex numbers As shown in Figure 4.5, the geometric interpretation of complex addition is the “parallelogram rule,” where z; + zg lies on the node of a parallelogram formed from z, and z2. For complex subtraction, —z2 is represented in the complex plane by reversing its direction and then adding to z, using the parallelogram rule, as shown in Figure 4.5. If z is given in polar or complex exponential form, it must be converted to rectangular form to perform the addition. In MATLAB, complex addition is performed in the same way as it is performed for real numbers:
Figure 4.5: Addition and subtraction of complex numbers As shown in Figure 4.5, the geometric interpretation of complex addition is the “parallelogram rule,” where z; + zg lies on the node of a parallelogram formed from z, and z2. For complex subtraction, —z2 is represented in the complex plane by reversing its direction and then adding to z, using the parallelogram rule, as shown in Figure 4.5. If z is given in polar or complex exponential form, it must be converted to rectangular form to perform the addition. In MATLAB, complex addition is performed in the same way as it is performed for real numbers:
Figure 4.6: Rotation of complex numbers As shown in Figure 4.6, 2122 is just a rotation of z, through the angle 62 . A particular case o rotation results from the multiplication by 7. Recalling that e’"/2 = 7, the product jz, become
Figure 4.6: Rotation of complex numbers As shown in Figure 4.6, 2122 is just a rotation of z, through the angle 62 . A particular case o rotation results from the multiplication by 7. Recalling that e’"/2 = 7, the product jz, become
Figure 4.7: Complex conjugate Plotting z* in the complex plane, by replacing y with —y , as shown in Figure 4.7, we see that the angle or phase of z* is —0. Thus,
Figure 4.7: Complex conjugate Plotting z* in the complex plane, by replacing y with —y , as shown in Figure 4.7, we see that the angle or phase of z* is —0. Thus,
Figure 4.8: Simple plot of a complex number
Figure 4.8: Simple plot of a complex number
The axis command provides control over the scaling and appearance of both the horizontal and vertical axes of a plot. This command has many features, so only the most useful will be discussed here. For more complete information, refer to on-line help. The primary features are given in the following table
The axis command provides control over the scaling and appearance of both the horizontal and vertical axes of a plot. This command has many features, so only the most useful will be discussed here. For more complete information, refer to on-line help. The primary features are given in the following table
There are several commands to control the appearance of the plot. These include: Printing Figures and Saving Figure Files
There are several commands to control the appearance of the plot. These include: Printing Figures and Saving Figure Files
Figure 4.9: Complex number plot The plot that results is shown in Figure 4.9.
Figure 4.9: Complex number plot The plot that results is shown in Figure 4.9.
Figure 4.10: Compass plot of complex number addition
Figure 4.10: Compass plot of complex number addition
In this case, the next value to be generated would be 10, but this is not included in the output since it would be greater than 9, the value of end.
In this case, the next value to be generated would be 10, but this is not included in the output since it would be greater than 9, the value of end.
When two arrays have the same dimensions, addition, subtraction, multiplication, and division ap- ply on an element-by-element basis. The notation for some operations is somewhat unconventional. Element-by-Element Array-Array Mathematics
When two arrays have the same dimensions, addition, subtraction, multiplication, and division ap- ply on an element-by-element basis. The notation for some operations is somewhat unconventional. Element-by-Element Array-Array Mathematics
Figure 5.1: Height-time graph of vertical motion under gravity Another way to generate multiple curves on one plot is to use a single matrix with multiple columns as the second argument of the plot command. Thus, the command plot (x,F), where x is a vector and F is a matrix, will produce a graph in which each column of F is a curve plotted against x.
Figure 5.1: Height-time graph of vertical motion under gravity Another way to generate multiple curves on one plot is to use a single matrix with multiple columns as the second argument of the plot command. Thus, the command plot (x,F), where x is a vector and F is a matrix, will produce a graph in which each column of F is a curve plotted against x.
Figure 5.2: Plot with two polynomial curves
Figure 5.2: Plot with two polynomial curves
Figure 5.3: Plot with two polynomial curves, separate y axes
Figure 5.3: Plot with two polynomial curves, separate y axes
Figure 5.4: Plot with four polynomial subplots The resulting plot is shown in Figure 5.4.
Figure 5.4: Plot with four polynomial subplots The resulting plot is shown in Figure 5.4.
Figure 6.1: Sinusoidal signal A cos(wt + 8) with —7/2<6< 0. When 7 is positive, it is a “time delay,” that describes the time (greater than zero) when the first peak is reached. When 7 is negative, it is a “time advance” that describes the time (less than zero) when the last peak was achieved. This sinusoidal signal is shown in Figure 6.1. Consider computing and plotting the following cosine signal
Figure 6.1: Sinusoidal signal A cos(wt + 8) with —7/2<6< 0. When 7 is positive, it is a “time delay,” that describes the time (greater than zero) when the first peak is reached. When 7 is negative, it is a “time advance” that describes the time (less than zero) when the last peak was achieved. This sinusoidal signal is shown in Figure 6.1. Consider computing and plotting the following cosine signal
Figure 6.2: Sinusoidal signals
Figure 6.2: Sinusoidal signals
The phasor representation of the sinusoid x(t) = A cos(wt+¢) is shown in the complex plane in Fig- ure 6.3. At t = 0, the complex representation produces the phasor Ae/?, where ¢ is approximately —/10. If time t increases to time t,, then the complex representation produces meaning that the signal «(t) may be reconstructed from Ae’? by multiplying by e/”¢ and taking the real part.
The phasor representation of the sinusoid x(t) = A cos(wt+¢) is shown in the complex plane in Fig- ure 6.3. At t = 0, the complex representation produces the phasor Ae/?, where ¢ is approximately —/10. If time t increases to time t,, then the complex representation produces meaning that the signal «(t) may be reconstructed from Ae’? by multiplying by e/”¢ and taking the real part.
Figure 6.4: Reading a real signal from a complex, rotating phasor
Figure 6.4: Reading a real signal from a complex, rotating phasor
Figure 6.6: Scotch yoke mechanism Figure 6.5: The signals e/#*, Re [e/#¢], and Im [e/#*]
Figure 6.6: Scotch yoke mechanism Figure 6.5: The signals e/#*, Re [e/#¢], and Im [e/#*]
Figure 6.7: Adding phasors Beating Between Tones If you have heard two slightly mistuned musical instruments playing pure tones whose frequencies were close but not equal, you have sensed a beating phenomenon in which you perceive a single pure tone whose amplitude slowly varies periodically. The single perceived tone can be shown to have a frequency that is the average of the two mismatched frequencies, amplitude modulated by a tone whose “beat” frequency is half the difference between the two mismatched frequencies. This effect is shown in Figure 6.8.
Figure 6.7: Adding phasors Beating Between Tones If you have heard two slightly mistuned musical instruments playing pure tones whose frequencies were close but not equal, you have sensed a beating phenomenon in which you perceive a single pure tone whose amplitude slowly varies periodically. The single perceived tone can be shown to have a frequency that is the average of the two mismatched frequencies, amplitude modulated by a tone whose “beat” frequency is half the difference between the two mismatched frequencies. This effect is shown in Figure 6.8.
Figure 6.8: Beating between tones
Figure 6.8: Beating between tones
The following is the script to simulate beating tones in which wo = 27 x 1400 rad/s and w, = 27 x 100 rad/s, resulting in the plot shown in Figure 6.8.
The following is the script to simulate beating tones in which wo = 27 x 1400 rad/s and w, = 27 x 100 rad/s, resulting in the plot shown in Figure 6.8.
Polynomial Operations By characterizing polynomial A(s) by coefficients a, stored in vector a and polynomial B(s) by coefficients by, stored in vector b, algebraic operations can be performed on the two polynomials.
Polynomial Operations By characterizing polynomial A(s) by coefficients a, stored in vector a and polynomial B(s) by coefficients by, stored in vector b, algebraic operations can be performed on the two polynomials.
Suppose that root r; of A(s) is repeated p times and the other n—p roots (denoted rp41, %p42,---3Tn are distinct. Then H(s) has the partial fraction expansion Again consider the rational function
Suppose that root r; of A(s) is repeated p times and the other n—p roots (denoted rp41, %p42,---3Tn are distinct. Then H(s) has the partial fraction expansion Again consider the rational function
Figure 6.10: Function of two variables plotted in three dimensions grid of xy values. To evaluate a function of two variables f(x,y), first define a two-dimensional grid in the ry plane, then evaluate the function at the grid points to determine points on the three-dimensional surface. This is shown in Figure 6.10, where the surface values z = f(x,y) are plotted above the ornid of 77) values. \ two-dimensional grid in the ry plane is defined in MATLAB by two vectors, one containing the r-coordinates at all the points in the grid, and the other containing the y-coordinates. For example, o define a grid in x varying from —2 to 2 in increments of 1 and a grid in y varying from —1 to 2 n increments of 1, using colon notation:
Figure 6.10: Function of two variables plotted in three dimensions grid of xy values. To evaluate a function of two variables f(x,y), first define a two-dimensional grid in the ry plane, then evaluate the function at the grid points to determine points on the three-dimensional surface. This is shown in Figure 6.10, where the surface values z = f(x,y) are plotted above the ornid of 77) values. \ two-dimensional grid in the ry plane is defined in MATLAB by two vectors, one containing the r-coordinates at all the points in the grid, and the other containing the y-coordinates. For example, o define a grid in x varying from —2 to 2 in increments of 1 and a grid in y varying from —1 to 2 n increments of 1, using colon notation:
After the underlying grid matrices have been defined, the corresponding function values can be computed. For example, for the following function the function values would be computed as
After the underlying grid matrices have been defined, the corresponding function values can be computed. For example, for the following function the function values would be computed as
Figure 6.11: Mesh and surface plots of a function of two variables Note that the xy grid has been made finer by incrementing both x and y by 0.1. Also note that the arguments X and Y could have been replaced by x and y in both the mesh and surf commands. The resulting plots are shown in Figure 6.11. Note that you need to know something about the
Figure 6.11: Mesh and surface plots of a function of two variables Note that the xy grid has been made finer by incrementing both x and y by 0.1. Also note that the arguments X and Y could have been replaced by x and y in both the mesh and surf commands. The resulting plots are shown in Figure 6.11. Note that you need to know something about the
Figure 6.12: Contour plot of a function of two variables The commands to produce the mesh/contour plot shown in Figure 6.13 from the function f (2,1 considered in the examples above:
Figure 6.12: Contour plot of a function of two variables The commands to produce the mesh/contour plot shown in Figure 6.13 from the function f (2,1 considered in the examples above:
Figure 6.13: Mesh/contour plot of a function of two variables
Figure 6.13: Mesh/contour plot of a function of two variables
Figure 6.14: Plot of the function y = 0.025a2° — 0.062524 — 0.33323 4+ x The result. corresponds to the “valley” shown in the plot, but which is not the minimum point on this interval, which is at the boundary « = 1. The fminbnd function first looks for a minimum point corresponding to a zero slope; if it finds one, it stops. If it does not find one, it looks at the function values at the boundaries of the specified interval for x. In this example, a zero-slope minimum was found, so the true minimum at the boundary was missed.
Figure 6.14: Plot of the function y = 0.025a2° — 0.062524 — 0.33323 4+ x The result. corresponds to the “valley” shown in the plot, but which is not the minimum point on this interval, which is at the boundary « = 1. The fminbnd function first looks for a minimum point corresponding to a zero slope; if it finds one, it stops. If it does not find one, it looks at the function values at the boundaries of the specified interval for x. In this example, a zero-slope minimum was found, so the true minimum at the boundary was missed.
Figure 7.1: Random sequences
Figure 7.1: Random sequences
cost if the cylindrical portion costs $300/m? of surface area and the hemispherical portion costs $400/m?. Figure 7.2: Tank configuration
cost if the cylindrical portion costs $300/m? of surface area and the hemispherical portion costs $400/m?. Figure 7.2: Tank configuration
Figure 7.3: Tank design problem: cost versus radius Note that the radius corresponding to minimum cost (Rmin = 4.9240 is close to the approximate value, Rest = 4.5708 that we computed to assist in the selection of a range of R to investigate. The plot of cost C' versus radius R is shown in Figure 7.3.
Figure 7.3: Tank design problem: cost versus radius Note that the radius corresponding to minimum cost (Rmin = 4.9240 is close to the approximate value, Rest = 4.5708 that we computed to assist in the selection of a range of R to investigate. The plot of cost C' versus radius R is shown in Figure 7.3.
Figure 7.4: Absolute frequency histograms
Figure 7.4: Absolute frequency histograms
Figure 7.5: Relative frequency histograms Measures of central tendency
Figure 7.5: Relative frequency histograms Measures of central tendency
The rand function in MATLAB generates uniform random numbers distributed over the interval [0,1]. A state vector is used to generate a random sequence of values. A given state vector always generates the same random sequence. Thus, the sequences are known more formally as pseudorandom sequences. This generator can generate all the floating point numbers in the closed interval [2~—°%, 1 — 2-53]. Theoretically, it can generate over 2!49? values before repeating itself. The following commands generate and display two sets of ten random numbers uniformly distributed between 0 and 1; the difference between the two sets is caused by the different states.
The rand function in MATLAB generates uniform random numbers distributed over the interval [0,1]. A state vector is used to generate a random sequence of values. A given state vector always generates the same random sequence. Thus, the sequences are known more formally as pseudorandom sequences. This generator can generate all the floating point numbers in the closed interval [2~—°%, 1 — 2-53]. Theoretically, it can generate over 2!49? values before repeating itself. The following commands generate and display two sets of ten random numbers uniformly distributed between 0 and 1; the difference between the two sets is caused by the different states.
The histogram plot is shown in Figure 7.6. Note that in this trial, there were 26 tails and 24 heads, close to the expected result. Since this is a random process, repeated trials will give different results. The output displayed by the script:
The histogram plot is shown in Figure 7.6. Note that in this trial, there were 26 tails and 24 heads, close to the expected result. Since this is a random process, repeated trials will give different results. The output displayed by the script:
Figure 7.6: Histogram of 50 Coin Flips Gaussian Random Numbers
Figure 7.6: Histogram of 50 Coin Flips Gaussian Random Numbers
To generate a row vector x of 500 Gaussian random variables with mean m and standard deviation 8:
To generate a row vector x of 500 Gaussian random variables with mean m and standard deviation 8:
Figure 7.7: Normalized Gaussian distribution
Figure 7.7: Normalized Gaussian distribution
Figure 7.1: Random sequences
Figure 7.1: Random sequences
cost if the cylindrical portion costs $300/m? of surface area and the hemispherical portion costs $400/m?. Figure 7.2: Tank configuration
cost if the cylindrical portion costs $300/m? of surface area and the hemispherical portion costs $400/m?. Figure 7.2: Tank configuration
Figure 7.3: Tank design problem: cost versus radius Note that the radius corresponding to minimum cost (Rmin = 4.9240 is close to the approximate value, Rest = 4.5708 that we computed to assist in the selection of a range of R to investigate. The plot of cost C' versus radius R is shown in Figure 7.3.
Figure 7.3: Tank design problem: cost versus radius Note that the radius corresponding to minimum cost (Rmin = 4.9240 is close to the approximate value, Rest = 4.5708 that we computed to assist in the selection of a range of R to investigate. The plot of cost C' versus radius R is shown in Figure 7.3.
Figure 7.4: Absolute frequency histograms
Figure 7.4: Absolute frequency histograms
Figure 7.5: Relative frequency histograms Measures of central tendency
Figure 7.5: Relative frequency histograms Measures of central tendency
The rand function in MATLAB generates uniform random numbers distributed over the interval [0,1]. A state vector is used to generate a random sequence of values. A given state vector always generates the same random sequence. Thus, the sequences are known more formally as pseudorandom sequences. This generator can generate all the floating point numbers in the closed interval [2~—°%, 1 — 2-53]. Theoretically, it can generate over 2!49? values before repeating itself. The following commands generate and display two sets of ten random numbers uniformly distributed between 0 and 1; the difference between the two sets is caused by the different states.
The rand function in MATLAB generates uniform random numbers distributed over the interval [0,1]. A state vector is used to generate a random sequence of values. A given state vector always generates the same random sequence. Thus, the sequences are known more formally as pseudorandom sequences. This generator can generate all the floating point numbers in the closed interval [2~—°%, 1 — 2-53]. Theoretically, it can generate over 2!49? values before repeating itself. The following commands generate and display two sets of ten random numbers uniformly distributed between 0 and 1; the difference between the two sets is caused by the different states.
The histogram plot is shown in Figure 7.6. Note that in this trial, there were 26 tails and 24 heads, close to the expected result. Since this is a random process, repeated trials will give different results. The output displayed by the script:
The histogram plot is shown in Figure 7.6. Note that in this trial, there were 26 tails and 24 heads, close to the expected result. Since this is a random process, repeated trials will give different results. The output displayed by the script:
Figure 7.6: Histogram of 50 Coin Flips Gaussian Random Numbers
Figure 7.6: Histogram of 50 Coin Flips Gaussian Random Numbers
To generate a row vector x of 500 Gaussian random variables with mean m and standard deviation 8:
To generate a row vector x of 500 Gaussian random variables with mean m and standard deviation 8:
Figure 7.7: Normalized Gaussian distribution
Figure 7.7: Normalized Gaussian distribution
Relational operators can be used to compare two arrays of the same size or to compare an array to a scalar. In the second case, the scalar is compared with all elements of the array and the result has the same size as the array. For more information: help ops. Relational Operators
Relational operators can be used to compare two arrays of the same size or to compare an array to a scalar. In the second case, the scalar is compared with all elements of the array and the result has the same size as the array. For more information: help ops. Relational Operators
The precedence from highest to lowest is relational operators, followed by logical operators ~, &, and |. Parentheses can be used to change the precedence and should be used liberally to clarify the operations. Definitions of the logical operators, with 0 representing False and 1 representing True:
The precedence from highest to lowest is relational operators, followed by logical operators ~, &, and |. Parentheses can be used to change the precedence and should be used liberally to clarify the operations. Definitions of the logical operators, with 0 representing False and 1 representing True:
Figure 8.1: Plot of a discontinuous signal The plot that is generated is shown in Figure 8.1. Relational and Logical Functions MATLAB provides several useful relational and local functions that operate on scalars, vectors, and matrices. The following is a partial list of these functions.
Figure 8.1: Plot of a discontinuous signal The plot that is generated is shown in Figure 8.1. Relational and Logical Functions MATLAB provides several useful relational and local functions that operate on scalars, vectors, and matrices. The following is a partial list of these functions.
The resulting graph is shown in Figure 8.2. Note that the initial slope of the temperature is steer and that temperature of the soda changes rapidly. As the difference between the soda temperature and the refrigerator temperature becomes smaller, that change in soda temperature slows. Exact Solution This cooling problem has an exact mathematical solution, which is determined by expressing the update relationship as a differential equation and then solving the resulting differential equation. The result is
The resulting graph is shown in Figure 8.2. Note that the initial slope of the temperature is steer and that temperature of the soda changes rapidly. As the difference between the soda temperature and the refrigerator temperature becomes smaller, that change in soda temperature slows. Exact Solution This cooling problem has an exact mathematical solution, which is determined by expressing the update relationship as a differential equation and then solving the resulting differential equation. The result is
Figure 8.3: 3-point moving average signal filtering If more input signal samples were averaged, the noise would be further reduced. However, this would also lead to distortion of the original signal. Thus, filtering functions must be designed tc compromise between noise reduction and signal distortion.
Figure 8.3: 3-point moving average signal filtering If more input signal samples were averaged, the noise would be further reduced. However, this would also lead to distortion of the original signal. Thus, filtering functions must be designed tc compromise between noise reduction and signal distortion.
The number of zero crossings is the number of times that x(k) and x(k + 1) differ in sign, or tl number of times that x(k) -2(k+1) <0. The statistical measurements should include the mean, variance, average magnitude, average power and number of zero crossings. If the data sequence is z(n), n = 1,...,.N, these measurements are defined as follows:
The number of zero crossings is the number of times that x(k) and x(k + 1) differ in sign, or tl number of times that x(k) -2(k+1) <0. The statistical measurements should include the mean, variance, average magnitude, average power and number of zero crossings. If the data sequence is z(n), n = 1,...,.N, these measurements are defined as follows:
The resulting plots are shown in Figure 8.4. Figure 8.4: Data sequence and histogram for the spoken word ‘zero’
The resulting plots are shown in Figure 8.4. Figure 8.4: Data sequence and histogram for the spoken word ‘zero’
Vectors with the same number of elements can be added and subtracted in a very natural way Vector Addition
Vectors with the same number of elements can be added and subtracted in a very natural way Vector Addition
Figure 9.1: A vector in R® Geometrically, R” is n-dimensional space, and the notation x € R” means that x is a point in that space, specified by the n coordinates 71, 22,...%n. Figure 9.1 shows a vector in R°, drawn as an arrow from the origin to the point x.
Figure 9.1: A vector in R® Geometrically, R” is n-dimensional space, and the notation x € R” means that x is a point in that space, specified by the n coordinates 71, 22,...%n. Figure 9.1 shows a vector in R°, drawn as an arrow from the origin to the point x.
Figure 9.3: Ship course components
Figure 9.3: Ship course components
Scalar element a;; is located in row 2 and column 7. A square matrix has the same number of row and columns (m = n). The main diagonal of a matrix consists of the elements aj; (the two subscripts are equal). The main diagonal runs diagonally down and away from the top left corner of the matrix, but it does not end in the lower right corner unless the matrix is square. A matrix is denoted by a boldfaced capital letter having elements in the corresponding lower-case letter having double subscripts for the row and column. An m x n (read m by n) matrix, having m rows and n columns
Scalar element a;; is located in row 2 and column 7. A square matrix has the same number of row and columns (m = n). The main diagonal of a matrix consists of the elements aj; (the two subscripts are equal). The main diagonal runs diagonally down and away from the top left corner of the matrix, but it does not end in the lower right corner unless the matrix is square. A matrix is denoted by a boldfaced capital letter having elements in the corresponding lower-case letter having double subscripts for the row and column. An m x n (read m by n) matrix, having m rows and n columns
To generate an identity matrix in MATLAB: An identity matrix is a matrix with ones on the main diagonal and zeros elsewhere. The following is an identity matrix with four rows and four columns
To generate an identity matrix in MATLAB: An identity matrix is a matrix with ones on the main diagonal and zeros elsewhere. The following is an identity matrix with four rows and four columns
To better understand this operation, represent a matrix as a collection of vectors. For example, each row of A can be represented as being a vector Alternatively, each column of B can be represented as a vector
To better understand this operation, represent a matrix as a collection of vectors. For example, each row of A can be represented as being a vector Alternatively, each column of B can be represented as a vector
Matrix-matrix multiplication can now be defined in terms of inner products of these vectors. An n-element column vector x is an n x 1 matrix, whose transpose x isalxn matrix, or n-element row vector
Matrix-matrix multiplication can now be defined in terms of inner products of these vectors. An n-element column vector x is an n x 1 matrix, whose transpose x isalxn matrix, or n-element row vector
For example, consider the following matrices Applying this result to equation (9.1) and using representation of matrices A and B as the collec- tions of vectors defined in equations (9.3) and (9.4) Observe that this is the inner product of x and y where m Xn =2 xX 3 and nx p=3 x 3. Computing elements of C:
For example, consider the following matrices Applying this result to equation (9.1) and using representation of matrices A and B as the collec- tions of vectors defined in equations (9.3) and (9.4) Observe that this is the inner product of x and y where m Xn =2 xX 3 and nx p=3 x 3. Computing elements of C:
In MATLAB matrix multiplication is denoted by an asterisk. The two inner numbers are not the same, so BA does not exist. Note that this implies that matrix multiplication does not commute
In MATLAB matrix multiplication is denoted by an asterisk. The two inner numbers are not the same, so BA does not exist. Note that this implies that matrix multiplication does not commute
Two scalars are called inverses when their product is 1, such as 5 and 0.2. Two square matrices are inverses of one another if their product is the identity matrix
Two scalars are called inverses when their product is 1, such as 5 and 0.2. Two square matrices are inverses of one another if their product is the identity matrix
In the outer product, the inner products that define its elements are between the one-dimensional row vectors of x and the one-dimensional column vectors of y”. Another matrix multiplication involving two vectors is the outer product, which is the produc of a row vector and a column vector. If x and y are n-element column vectors, then the produc xy! isan x 1 matrix multiplying an 1 x n matrix, yielding an n x n result If Lis an n x n identity matrix and A is an n x n matrix, then
In the outer product, the inner products that define its elements are between the one-dimensional row vectors of x and the one-dimensional column vectors of y”. Another matrix multiplication involving two vectors is the outer product, which is the produc of a row vector and a column vector. If x and y are n-element column vectors, then the produc xy! isan x 1 matrix multiplying an 1 x n matrix, yielding an n x n result If Lis an n x n identity matrix and A is an n x n matrix, then
The system of equations can also be expressed using row vectors for B and x. For example, the set of equations above can be written as This system of equations can be rewritten in matrix form as
The system of equations can also be expressed using row vectors for B and x. For example, the set of equations above can be written as This system of equations can be rewritten in matrix form as
Figure 9.4: The graphs of two equations intersect at the solution For example the two equations
Figure 9.4: The graphs of two equations intersect at the solution For example the two equations
Figure 9.5: Parallel graphs indicate that no solution exists Note that slopes of the two lines are both equal to 3/4, so the lines are parallel, as shown in Figure 9.5. Since they do not intersect, no solution exists. The matrix A is the same as that in the one-line singular case above, where this matrix was found to be singular. An ill-conditioned set of equations is a set that is close to being singular (for example, two equa- tions whose graphs are close to being parallel). The solution to an ill-conditioned set of equations depends on the accuracy with which the calculations are made. No computer can represent a number with infinitely many significant figures, and so a given set of equations can appear to be singular if the accuracy required to solve them is greater than the number of significant figures used by the software. An ill-conditioned set of equations is a set that is close to being singular (for example, two equa-
Figure 9.5: Parallel graphs indicate that no solution exists Note that slopes of the two lines are both equal to 3/4, so the lines are parallel, as shown in Figure 9.5. Since they do not intersect, no solution exists. The matrix A is the same as that in the one-line singular case above, where this matrix was found to be singular. An ill-conditioned set of equations is a set that is close to being singular (for example, two equa- tions whose graphs are close to being parallel). The solution to an ill-conditioned set of equations depends on the accuracy with which the calculations are made. No computer can represent a number with infinitely many significant figures, and so a given set of equations can appear to be singular if the accuracy required to solve them is greater than the number of significant figures used by the software. An ill-conditioned set of equations is a set that is close to being singular (for example, two equa-
Illustrated in Figure 9.6 is a robot arm having two “links” connected by two “joints”: a shoulder, or base, joint, and an elbow joint. There is a motor at each joint and the joint angles are 6, and 62. The (#1, #2) coordinates of the hand at the end of the arm are given by The problem is to determine how to control the joint angles by the motors to move the hand from one position to another. The arm is to start from rest at a known position and move to a desired position. It must start and stop with zero velocity and acceleration. The following polynomial expressions are to be used for controlling the motion by generating commands to be sent to the
Illustrated in Figure 9.6 is a robot arm having two “links” connected by two “joints”: a shoulder, or base, joint, and an elbow joint. There is a motor at each joint and the joint angles are 6, and 62. The (#1, #2) coordinates of the hand at the end of the arm are given by The problem is to determine how to control the joint angles by the motors to move the hand from one position to another. The arm is to start from rest at a known position and move to a desired position. It must start and stop with zero velocity and acceleration. The following polynomial expressions are to be used for controlling the motion by generating commands to be sent to the
Figure 9.7: Calculated path of robot hand
Figure 9.7: Calculated path of robot hand
To illustrate linear regression, consider the following set of temperature measurements: Linear Regression
To illustrate linear regression, consider the following set of temperature measurements: Linear Regression
Ignoring the constant factors 2/N and writing the two equations above in matrix form with the The MSE is minimum when its partial derivatives with respect to each of the coefficients are zero.
Ignoring the constant factors 2/N and writing the two equations above in matrix form with the The MSE is minimum when its partial derivatives with respect to each of the coefficients are zero.
This is a pair of linear equations in two unknowns that can be solved using the methods discussed in the previous section of these notes. The values of a; and ag determined in this way represent the straight line with the minimum mean squared error. terms in the unknown coefficients a, and a2 on the left hand side The MATLAB command to compute the best linear fit to a set of data is polyfit(x,y,1), which returns a coefficient vector of the straight line fitting the data. For the data above:
This is a pair of linear equations in two unknowns that can be solved using the methods discussed in the previous section of these notes. The values of a; and ag determined in this way represent the straight line with the minimum mean squared error. terms in the unknown coefficients a, and a2 on the left hand side The MATLAB command to compute the best linear fit to a set of data is polyfit(x,y,1), which returns a coefficient vector of the straight line fitting the data. For the data above:
Figure 10.2: Linear regression curve fit
Figure 10.2: Linear regression curve fit
Figure 10.3: Polynomial Curve Fitting coefficients and the alternating signs on the coefficients. This example clearly demonstrates th: difficulties with higher-degree polynomials.
Figure 10.3: Polynomial Curve Fitting coefficients and the alternating signs on the coefficients. This example clearly demonstrates th: difficulties with higher-degree polynomials.
Figure 10.4: Flow rate and volume for a coffee pot In linear interpolation, the estimate is based on a straight line connecting neighboring pairs of data points. In cubic-spline interpolation, the estimate is based on joining neighboring data points with a cubic (third-degree) polynomial.
Figure 10.4: Flow rate and volume for a coffee pot In linear interpolation, the estimate is based on a straight line connecting neighboring pairs of data points. In cubic-spline interpolation, the estimate is based on joining neighboring data points with a cubic (third-degree) polynomial.
Figure 10.6: Comparison of linear and cubic spline interpolation
Figure 10.6: Comparison of linear and cubic spline interpolation
Figure 10.7: Plot of Threshold of Human Hearing
Figure 10.7: Plot of Threshold of Human Hearing
Figure 10.8: Plot of threshold of human hearing near minimum
Figure 10.8: Plot of threshold of human hearing near minimum
Figure 11.1: Integral of f(z) from a to b integral, and the method is known as the trapezoidal rule. If the function is estimated with piecewise quadratic functions, the technique is called Simpson’s rule. Trapezoidal Rule
Figure 11.1: Integral of f(z) from a to b integral, and the method is known as the trapezoidal rule. If the function is estimated with piecewise quadratic functions, the technique is called Simpson’s rule. Trapezoidal Rule
Figure 11.3: Trapezoidal integration of humps(x) To illustrate integration, consider the function humps(x), a demonstration function provided by MATLAB that has strong peaks near « = 0.3 and x = 0.9, shown in Figure 11.3. Using computed samples of the humps function, the function trapz approximates the area using the trapezoidal approximation. For n = 18 subintervals, the integral approximation is given by:
Figure 11.3: Trapezoidal integration of humps(x) To illustrate integration, consider the function humps(x), a demonstration function provided by MATLAB that has strong peaks near « = 0.3 and x = 0.9, shown in Figure 11.3. Using computed samples of the humps function, the function trapz approximates the area using the trapezoidal approximation. For n = 18 subintervals, the integral approximation is given by:
Figure 11.2: Approximation of f(x) from x; to x41
Figure 11.2: Approximation of f(x) from x; to x41
A script to estimate the velocity: An accelerometer measures acceleration and the resulting measurements can be used to estimate velocity and displacement. The acceleration is integrated to estimate velocity and the velocity in turn is integrated to estimate displacement. Suppose a vehicle starts from rest at time t = 0 and that its measured acceleration is given by the following table:
A script to estimate the velocity: An accelerometer measures acceleration and the resulting measurements can be used to estimate velocity and displacement. The acceleration is integrated to estimate velocity and the velocity in turn is integrated to estimate displacement. Suppose a vehicle starts from rest at time t = 0 and that its measured acceleration is given by the following table:
The integral of this function over limits 0 < « <a, -—7 <y <7 is given by
The integral of this function over limits 0 < « <a, -—7 <y <7 is given by
Figure 11.6: Derivative of f(a) at x =a. The derivative f’(x) can be interpreted geometrically as the slope of the function at (x, f(x)), where the slope of f(x) is the slope of the tangent line to the function at x, as shown in Figure 11.6. Formally, the derivative of f at x is
Figure 11.6: Derivative of f(a) at x =a. The derivative f’(x) can be interpreted geometrically as the slope of the function at (x, f(x)), where the slope of f(x) is the slope of the tangent line to the function at x, as shown in Figure 11.6. Formally, the derivative of f at x is
Figure 11.7: Techniques for approximating f/(;). Note that the forward difference at xz is the same as the backward difference at r,41. The quality of both of these derivative approximations depends heavily on two factors: the spacing of the samples and the scatter in the data due to measurement error. The greater the spacing, the more difficult it is to estimate the derivative. The approximation improves as the spacing between the two points decreases.
Figure 11.7: Techniques for approximating f/(;). Note that the forward difference at xz is the same as the backward difference at r,41. The quality of both of these derivative approximations depends heavily on two factors: the spacing of the samples and the scatter in the data due to measurement error. The greater the spacing, the more difficult it is to estimate the derivative. The approximation improves as the spacing between the two points decreases.
Figure 11.8: Measurements of a sine signal containing Gaussian distributed random errors
Figure 11.8: Measurements of a sine signal containing Gaussian distributed random errors
Figure 11.9: Comparison of backward difference and central difference methods Determine the linear acceleration of an object whose speed is defined by s(t) = t? — 2t? + 2 m/s where ¢ is in seconds, over the interval 0 to 5. Determine the specific acceleration at t = 2.5s.
Figure 11.9: Comparison of backward difference and central difference methods Determine the linear acceleration of an object whose speed is defined by s(t) = t? — 2t? + 2 m/s where ¢ is in seconds, over the interval 0 to 5. Determine the specific acceleration at t = 2.5s.
Figure 11.10: Curve fitting and derivative approximation dashed lines in the bottom subplot, it is overwhelmingly apparent that approximating the derivative by finite differences can lead to poor results. Note that the derivative of the second order curve fit shown in the solid curve in the bottom subplot does not show the large fluctuations of the approximation.
Figure 11.10: Curve fitting and derivative approximation dashed lines in the bottom subplot, it is overwhelmingly apparent that approximating the derivative by finite differences can lead to poor results. Note that the derivative of the second order curve fit shown in the solid curve in the bottom subplot does not show the large fluctuations of the approximation.
The function datenum is used to compute a date number. It has three forms:
The function datenum is used to compute a date number. It has three forms:
A calendar can be generated for a desired month, for display in the Command window or to be placed in a 6-by-7 array. The functions tic and toc are used to time a sequence of commands. tic starts the timer; toc stops the timer and displays the elapsed time in seconds.
A calendar can be generated for a desired month, for display in the Command window or to be placed in a 6-by-7 array. The functions tic and toc are used to time a sequence of commands. tic starts the timer; toc stops the timer and displays the elapsed time in seconds.
Grand total is 20 elements using 650 bytes Symbolic variables and constants are represented by the data type symbolic object. For example, as displayed by the function whos for the symbolic variables and constants declared in the commands above:
Grand total is 20 elements using 650 bytes Symbolic variables and constants are represented by the data type symbolic object. For example, as displayed by the function whos for the symbolic variables and constants declared in the commands above:
The resulting plot is shown in Figure 13.3. Note that the expression plotted is automatically placed at the top of the plot and that the axis label for the independent variable is automatically placed. A ylabel command was used to label the dependent variable. Figure 13.1: Plot of a polynomial function using ezplot 4 Solving Algebraic and Transcendental Equations
The resulting plot is shown in Figure 13.3. Note that the expression plotted is automatically placed at the top of the plot and that the axis label for the independent variable is automatically placed. A ylabel command was used to label the dependent variable. Figure 13.1: Plot of a polynomial function using ezplot 4 Solving Algebraic and Transcendental Equations
Systems of linear equations can also be solved symbolically. Consider the following example that was previously solved numerically: The results are the same as those obtained numerically. For this problem, there is little advantage to finding the result symbolically. However, solving a system of equations with respect to a parameter, the symbolic approach provides an advantage. Consider the following set of equations
Systems of linear equations can also be solved symbolically. Consider the following example that was previously solved numerically: The results are the same as those obtained numerically. For this problem, there is little advantage to finding the result symbolically. However, solving a system of equations with respect to a parameter, the symbolic approach provides an advantage. Consider the following set of equations
Related topics:
MathematicsApplied MathematicsComputer ScienceSoftware EngineeringProgramming LanguagesArtificial IntelligenceStatisticsComputer EngineeringNumerical AnalysisCOMPUTER SCIENCE & ENGINEERING
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved