Papers by Antonio Rodríguez Diaz
In literature, we can find different metrics to evaluate the detected edges in digital images, li... more In literature, we can find different metrics to evaluate the detected edges in digital images, like Pratt's figure of merit (FOM), Jaccard’s index (JI) and Dice’s coefficient (DC). These metrics compare two images, the first one is the reference edges image, and the second one is the detected edges image. It is important to mention that all existing metrics must binarize images before their evaluation. Binarization step causes information to be lost because an incomplete image is being evaluated. In this paper, we propose a fuzzy index (FI) for edge evaluation that does not use a binarization step. In order to process all detected edges, images are represented in their fuzzy form and all calculations are made with fuzzy sets operators and fuzzy Euclidean distance between both images. Our proposed index is compared to the most used metrics using synthetic images, with good results.
Nowadays organizations work to improve their software development process, with a purpose to redu... more Nowadays organizations work to improve their software development process, with a purpose to reduce costs, improve quality and increase planning reliability. That is why decision making pertaining to role assignment in software engineering developing projects is one of the most important factors that affect the software development process in organizations. We should not only consider individual’s abilities and capabilities for better team performance but also consider knowing their personality traits to carry out the most suitable role in an effective working team. Through compilation of studies with RAMSET (Role Assignment Methodology for Software Engineering Teams) methodology some personalities and typologies have been identified to perform certain type of roles, thus helping us build a better, cohesive and less conflictive team. Our methodology based on personality has revealed appropriate and adequate personality patterns for assignment of best advisable performing roles in so...
Granular Computing, 2016
This paper proposes a new method for the formation of fuzzy higher type granular models. This is ... more This paper proposes a new method for the formation of fuzzy higher type granular models. This is accomplished by directly discovering uncertainty from a sample of numerical information. In this case the coefficient of variation is proposed as a heuristic for measuring uncertainty, where a direct relation between this measure and the footprint of uncertainty of an interval type-2 fuzzy membership function is given. Followed by a steepest descent algorithm which is used to calculate interval Sugeno consequents for a fuzzy inference system. Two synthetic and two real datasets are used, measuring the performance by evaluating the output interval coverage of the formed granular models as well as the coefficient of determination which assesses modeling performance.
PLOS ONE, 2015
In literature, we can find different metrics to evaluate the detected edges in digital images, li... more In literature, we can find different metrics to evaluate the detected edges in digital images, like Pratt's figure of merit (FOM), Jaccard's index (JI) and Dice's coefficient (DC). These metrics compare two images, the first one is the reference edges image, and the second one is the detected edges image. It is important to mention that all existing metrics must binarize images before their evaluation. Binarization step causes information to be lost because an incomplete image is being evaluated. In this paper, we propose a fuzzy index (FI) for edge evaluation that does not use a binarization step. In order to process all detected edges, images are represented in their fuzzy form and all calculations are made with fuzzy sets operators and fuzzy Euclidean distance between both images. Our proposed index is compared to the most used metrics using synthetic images, with good results.
We describe in this paper the optimization of the gains of a PID controller to stabilize the iner... more We describe in this paper the optimization of the gains of a PID controller to stabilize the inertia wheel pendulum (IWP) using bio-inspired and evolutionary methods. Particle swarm optimization and genetic algorithms are used to find the optimal gain values of the PID controller. Computer simulations and experiments are presented showing the control results using the optimal gain values to stabilize the inertia wheel pendulum. Both particle swarm optimization (PSO) and genetic algorithms (GAs) are shown to be effective tools for gain optimization of the inertia wheel.
Intelligent Decision Technologies, 2012
The choice of role selection for individuals in working team software developing projects is the ... more The choice of role selection for individuals in working team software developing projects is the basis of all Software Developing Process for organizations to be competitive in the industry. This paper proposes a Decision Making Fuzzy Model for Software Engineering Role Assignment based on Fuzzy Logic with Big Five Patterns using our RAMSET methodology. This model facilitates the role assignment decision making process and is a first approach for support toward assisting any organization or human resource personnel to select adequate new candidates for software engineering roles or reassign personnel already working in the organization to their best suited role for satisfactory performance.
Bio-inspired Optimization Methods of Fuzzy Logic Controllers Applied to Linear Plants
Advances in Intelligent and Soft Computing, 2010
We use an optimization method to find the parameters of the membership functions of a fuzzy logic... more We use an optimization method to find the parameters of the membership functions of a fuzzy logic controller (FLC) in order to minimize the steady state error for linear systems. The genetic algorithm (GA) and particle swarm optimization (PSO), which are optimization methods, were used to find the optimal FLC. The obtained FLC achieves regulation of the output and stability
Intelligent Control Using Interval Type-2 Fuzzy Logic
Intelligent Engineering Systems Through Artificial Neural Networks, Volume 17
Abstract In this paper, a class of Interval Type-2 Fuzzy Neural Networks (IT2FNN) is proposed, wh... more Abstract In this paper, a class of Interval Type-2 Fuzzy Neural Networks (IT2FNN) is proposed, which is functionally equivalent to interval type-2 fuzzy inference systems. The computational process envisioned for fuzzy neural systems is as follows: it starts with the ...
Studies in Fuzziness and Soft Computing, 2013
This paper proposes a new approach to simulating language evolution; it expands on the original w... more This paper proposes a new approach to simulating language evolution; it expands on the original work done by Lee and Zadeh on Fuzzy Grammars and introduces a Type-2 Fuzzy Grammar. Ants in an Ant Colony Optimization algorithm are given the ability of embedding a message on the pheromone using a Type-2 Fuzzy Grammar. These ants are able to gradually adopt a foreign language by adjusting the grades of membership of their grammar. Results that show the effect of uncertainty in a language are given.
Simulation of language evolution using Fuzzy Grammars
2012 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS), 2012
ABSTRACT Many computer simulations try to explain language evolution with socially oriented scena... more ABSTRACT Many computer simulations try to explain language evolution with socially oriented scenarios in multi-agent systems by applying methods such as genetic algorithms and neural networks. In this paper a new approach based on modifications to the classic Ant Colony Optimization algorithm is proposed. Ants are provided with a Fuzzy Grammar and the ability to embed a message in the pheromone. By fuzzifying the grammar, ants are able to modify the degree of membership of the production rules to gradually adopt a foreign language.
Advances in Soft Computing, 2011
In psychology projective tests are interpretative and subjective obtaining results based on the e... more In psychology projective tests are interpretative and subjective obtaining results based on the eye of the beholder, they are widely used because they yield rich and unique data and are very useful. Because measurement of drawing attributes have a degree of uncertainty it is possible to explore a fuzzy model approach to better assess interpretative results. This paper presents a study of the tree projective test applied in software development teams as part of RAMSET's (Role Assignment Methodology for Software Engineering Teams) methodology to assign specific roles to work in the team; using a Takagi-Sugeno-Kang (TSK) Fuzzy Inference System (FIS) and also training data applying an ANFIS model to our case studies we have obtained an application that can help in role assignment decision process recommending best suited roles for performance in software engineering teams.
Fuzzy Cellular Model for Predator-Prey Interaction Applied to the Control of Plagues in a Peppers Cropping
Studies in Computational Intelligence, 2010
The control of plagues is the regulation and the handling of some species referred to as plagues,... more The control of plagues is the regulation and the handling of some species referred to as plagues, normally these are species that affect the ecology and the economy of a certain location. The search for solutions to the important economic incidence of the plagues in the croppings has had an evolution throughout the last two decades. In the studies of
Genetic design of biped walking fuzzy logic controller
2009 IEEE Workshop on Hybrid Intelligent Models and Applications, 2009
This paper presents the automatic design, through a multi-objective genetic algorithm, of a fuzzy... more This paper presents the automatic design, through a multi-objective genetic algorithm, of a fuzzy logic controller (FLC) applied to walking of a biped robot. The design approach of the FLC comprises two aspects: tuning the antecedents and consequents membership functions parameters and obtain the antecedents and consequents fuzzy partition of a rule base with a fixed structure such that allow
Application of interval type-2 fuzzy neural networks in non-linear identification and time series prediction
Soft Computing, 2013
ABSTRACT Neural networks (NNs), type-1 fuzzy logic systems and interval type-2 fuzzy logic system... more ABSTRACT Neural networks (NNs), type-1 fuzzy logic systems and interval type-2 fuzzy logic systems (IT2FLSs) have been shown to be important methods in real world applications, which range from pattern recognition, time series prediction, to intelligent control. Recent research shows that embedding an IT2FLS on an NN can be very effective for a wide number of non-linear complex systems, especially when handling imperfect or incomplete information. In this paper we are presenting several models of interval type-2 fuzzy neural networks (IT2FNNs) that use a set of rules and interval type-2 membership functions for that purpose. Simulation results of non-linear function identification using the IT2FNN for one and three variables and for the Mackey–Glass chaotic time series prediction are presented to illustrate that the proposed models have potential for real world applications.
International Journal of Machine Learning and Cybernetics, 2013
In this paper we propose the use of a hybrid PSO-GA optimization method for automatic design of f... more In this paper we propose the use of a hybrid PSO-GA optimization method for automatic design of fuzzy logic controllers (FLC) to minimize the steady state error of a plant's response. We test the optimal FLC obtained by the hybrid PSO-GA method using benchmark control plants. The bio-inspired and the evolutionary methods are used to find the parameters of the membership functions of the FLC to obtain the optimal controller. Simulation results are obtained to show the feasibility of the proposed approach. A comparison is also made among the proposed Hybrid PSO-GA, GA and PSO to determine if there is a significant difference in the results.
Information Sciences, 2014
A new method for finding fuzzy information granules from multivariate data through a gravitationa... more A new method for finding fuzzy information granules from multivariate data through a gravitational inspired clustering algorithm is proposed in this paper. The proposed algorithm incorporates the theory of granular computing, which adapts the cluster size with respect to the context of the given data. Via an inspiration in Newton's law of universal gravitation, both conditions of clustering similar data and adapting to the size of each granule are achieved. This paper compares the Fuzzy Granular Gravitational Clustering Algorithm (FGGCA) against other clustering techniques on two grounds: classification accuracy, and clustering validity indices, e.g. Rand, FM, Davies-Bouldin, Dunn, Homogeneity, and Separation. The FGGCA is tested with multiple benchmark classification datasets, such as Iris, Wine, Seeds, and Glass identification.
Computer Applications in Engineering Education, 2011
Role Assignment Methodology for Software Engineering Teams (RAMSET) methodology relates personali... more Role Assignment Methodology for Software Engineering Teams (RAMSET) methodology relates personality, abilities, and software roles for building Software Engineering Teams, applying sociometric, and psychometric techniques. This paper presents the results and experience of applying RAMSET's software supporting tool developed under a fuzzy approach. This software facilitates the role assignment decision making process, which results in a choice of role selection for individuals in working team projects. It has been applied in Software Engineering Courses of our Computer Engineering Program with great success giving students a practical experience in learning objectives,
Medwave, 2014
Una revisión narrativa de las escalas de evaluación usadas para el diagnóstico del trastorno por ... more Una revisión narrativa de las escalas de evaluación usadas para el diagnóstico del trastorno por déficit de atención e hiperactividad en niños y adolescentes Narrative review of scales assessing attention-deficit/hyperactivity disorder in children and adolescents
Abstract—Neural Networks (NN), Type-1 Fuzzy Logic Systems (T1FLS) and Interval Type-2 Fuzzy Logic Systems (IT2FLS) are
universal approximators, they can approximate any non-linear function. Recent research shows that... more universal approximators, they can approximate any non-linear function. Recent research shows that embedding T1FLS on an NN or embedding IT2FLS on an NN can be very effective for a wide number of non-linear complex systems, especially when handling imperfect information. In this paper we show that an Interval Type-2 Fuzzy Neural Network (IT2FNN) is a universal approximator with some precision using a set of rules and Interval Type-2 membership functions (IT2MF) and the Stone-Weierstrass Theorem. Also, simulation results of non-linear function identification using the IT2FNN for one and three variables with 10-fold cross-validation are presented.
International Journal of Applied Evolutionary Computation, 2015
Organizations are complex systems, which are formed by other subsystems such as work teams, and a... more Organizations are complex systems, which are formed by other subsystems such as work teams, and are the focus of attention in this research. This article makes an approach to the teams involved software development process in IT companies using a viable system based model and computational modeling. An analysis of teamwork is made from a socio-technical perspective, where individuals and technology produce emergent behaviors that may be crucial to achieving goals, since fellowship, collaboration, and culture are relevant processes within these organizations and technology also playing an important role.
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Papers by Antonio Rodríguez Diaz