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Outline

Title
Abstract
Introduction
The Case for Imprecision
Limitations of Fuzzy Systems
The Allusion: Statistics and Random Processes
Max-Min Method
Other Similarity Methods
Other Forms of the Implication Operation
Case 1
Case 2
Gradient Method
Clustering Method
Clustering Method 229
Automated Methods for Fuzzy Systems
New Methods
Summary of Operations
Combs Method
SVD and Combs Method Examples
Rule-Base Reduction Methods
Combs Method for Rapid Inference
Error Analysis of the Methods
Multiobjective Decision Making
Fuzzy Bayesian Decision Method
Case 1. Crisp States and Actions
Case 2. Fuzzy States and Actions
Example Summary
Cluster Analysis
Feature Analysis
Interval Analysis in Arithmetic 457
Interval Analysis in Arithmetic
Approximate Methods of Extension
Vertex Method
Control System Design Problem
Assumptions in a Fuzzy Control System Design
Examples of Fuzzy Control System Design
Fuzzy Statistical Process Control 505
Measurement Data -Traditional SPC
Attribute Data -Traditional SPC
Summary
The Case of Fuzzy Data
References
All Topics
Computer Science
Artificial Intelligence

Fuzzy Logic with Engineering Applicaiton

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Abstract
sparkles

AI

This second edition of the book on fuzzy logic and its engineering applications discusses the advancements in fuzzy set theory since the first publication in 1995. It highlights the competitive nature of fuzzy systems with traditional linear algebra methods in speed and accuracy. The author emphasizes the careful usage of definitional terms related to uncertainty theories and addresses the misconceptions in the literature regarding axioms and laws. Several new sections have been added to capture recent developments in this rapidly evolving field, alongside corrected errata from the first edition.

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Fuzzy logic has emerged as a very powerful tool in dealing with complex problems. Recently the role of inference in handling uncertainty in engineering applications is gaining importance. Engineers and scientists are generally confronted with problems which are impossible to solve numerically using traditional mathematical rules. By making use of fuzzy logic , one can characterize and control a system whose model is not known or is ill- defined [11]. Fuzzy theory has the capability to capture the impreciseness of linguistic terms in statements of natural language. this provided with a greater capability to model human common-sense reasoning and decision making [14]. The idea of fuzzy logic was propounded by Lotfi Zadeh in 1965. His later works ‘A Rationale for Fuzzy Control’ and ‘Linguistic Approach’ (in 1972 and 1973 respectively) motivated the pioneering work done by other scientists. The first trial of fuzzy control was conducted by Mamdani on a laboratory steam engine (1974). In the 70’s this motivated the researchers to develop a series of applications of fuzzy control [3]. the work on derivation of fuzzy control rules (1983) was carried out by Takagi and Sugeno. Since mideighties research has been directed towards incorporating fuzzy at the hardware level itself [1], [7]. [11]. The work on fuzzy chip done by Togai Watanabe (1985) in an important milestone in this direction. An attempt has been made in this paper to inspire the reader to appreciate the use of fuzzy logic as a new logical tool that is instrumental in handling problems with ambiguities and where the information is imprecise and non numerical. the in adequacy of traditional Boolean logic to emulate the human thinking has also been highlighted.

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  235. 36. µ(s) = µ(a 8 ), for AC, S → I(a 6 , a 7 ), for DC, S → I(a 8 , A) 11.38. Scaling the pixel values between 0 and 1 by dividing by 255 we get the following: row 1 = 0.86, 0.12, 0.04, 0.06, 0.98; row 2 = 0.80, 0.90, 0, 0.94, 0.90; row 3 = 0.88, EXAMPLES IN (PAGE NUMBERS), Aerospace Engineering: 262, 449, 486, 577 Biotechnology: 55, 61, 69, 99, 138, 365, 589
  236. Chemical/Petroleum Engineering: 39, 105, 111, 158, 330, 334, 375, 384, 390, 493, 498, 500, 503
  237. Civil Engineering: 31, 32, 65, 67, 73, 74, 122, 131, 140, 238, 323, 327, 367, 388, 398, 541, 592, 596, 599
  238. Electrical Engineering: 62, 257, 338, 415, 418, 427, 579, 585 Environmental Engineering: 507, 513, 547
  239. Mechanical Engineering: 38, 60, 132, 133, 153, 404, 453
  240. PROBLEMS IN (END-OF-CHAPTER PROBLEM NUMBERS), Aerospace Engineering: 2.9, 10.5, 15.4 Biotechnology: 3.21, 5.27, 8.5, 11.3, 11.7, 11.14, 15.6
  241. Civil Engineering: 1.8, 1.12, 1.14, 2.2, 2.5, 2.6, 2.10, 3.3, 3.8, 3.16, 3.19, 3.20, 3.24, 4.12, 5.18, 5.24, 7.6, 8.4, 9.6, 10.7, 10.18, 10.20, 11.4, 11.5, 12.13, 12.15, 14.2, 14.3, 15.1, 15.8
  242. Geomatics: 3.23, 5.16, 5.25, 10.6, 10.11, 10.17, 11.6, 11.26, 11.29, 13.8 Materials Science: 4.9, 14.4
  243. Mechanical Engineering: 1.2, 2.1, 2.13, 3.15, 5.15, 5.28, 5.30, 5.34, 5.35, 10.10, 11.12, 12.7, 12.8, 13.1, 13.2, 13.4, 14.7, 15.3
  244. Miscellaneous Technologies: 1.1, 1.5, 1.16, 3.12, 3.13, 3.25, 3.26, 4.1, 4.4, 4.5, 4.6, 4.10, 5.7, 5.12, 5.13, 5.20, 5.23, 6.4, 6.7, 6.8, 6.9, 6.10, 6.11, 6.12, 7.1, 7.2, 7.3, 7.4, 7.5, 7.7, 9.1, 9.2, 9.5, 9.8, 9.10, 10.3, 10.8, 10.13, 10.21, 11.1, 11.2, 11.24, 11.25, 11.32, 11.33, 13.9, 14.8, 14.10, 15.2, 15.12

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