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Leticia Cervantes

Bio: Leticia Cervantes is an academic researcher. The author has contributed to research in topics: Fuzzy control system & Fuzzy logic. The author has an hindex of 11, co-authored 25 publications receiving 514 citations.

Papers
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Journal ArticleDOI
TL;DR: An optimization method is used to efficiently design the generalized type-2 fuzzy system to improve the control performance and the proposed method for control is applied to a non-linear control problem to test the advantages of the proposed approach.

207 citations

Journal ArticleDOI
TL;DR: This paper presents a proposed new approach for complex control combining several simpler individual fuzzy controllers, which has a hierarchical architecture with 2 levels (individual fuzzy systems and a superior control to adjust the global result).

167 citations

Book ChapterDOI
01 Jan 2010
TL;DR: A design of a fuzzy system for the longitudinal control of an F-14 airplane, which is of Mamdani type, is presented and results are compared against the PID controller.
Abstract: In this paper we present a design of a fuzzy system for the longitudinal control of an F-14 airplane. The longitudinal control is carried out only by controlling the elevators of the airplane. To carry out such monitoring it is necessary to use the stick, the rate of elevation and the angle of attack. These 3 variables are input into the fuzzy inference system, which is of Mamdani type, and we obtain as output the value of the elevators. After designing the fuzzy inference system we turn to the simulation stage. Simulation results of the longitudinal control are obtained using a plant in Simulink and those results are compared against the PID controller.

34 citations

Journal ArticleDOI
TL;DR: The performance of this approach based on fuzzy logic for the adaptation of gap generation and mutation probability in a genetic algorithm is presented with the benchmark problem of flight control and results show how it can decrease the error during the flight of an airplane using fuzzy Logic for some parameters of the genetic algorithm.
Abstract: We propose to use an approach based on fuzzy logic for the adaptation of gap generation and mutation probability in a genetic algorithm. The performance of this method is presented with the benchmark problem of flight control and results show how it can decrease the error during the flight of an airplane using fuzzy logic for some parameters of the genetic algorithm. In this case of study, we use fuzzy systems for adapting two parameters of the genetic algorithm to improve the design of a type 2 fuzzy controller and enhance its performance to achieve flight control. Finally, a statistical test is presented to prove the performance enhancement in the application using fuzzy adaptation in the genetic algorithm. It is important to mention that not only is this idea for control problems but also it can be used in pattern recognition and many different problems.

28 citations

Book ChapterDOI
26 Nov 2011
TL;DR: Simulation results of the proposed approach for intelligent control using a hierarchical modular architecture with type-2 fuzzy logic used for combining the outputs of the modules show that proposed approach has potential in solving complex control problems.
Abstract: In this paper we present simulation results that we have at this moment with a new approach for intelligent control of non-linear dynamical plants. First we present the proposed approach for intelligent control using a hierarchical modular architecture with type-2 fuzzy logic used for combining the outputs of the modules. Then, the approach is illustrated with two cases: aircraft control and shower control and in each problem we explain its behavior. Simulation results of the two case show that proposed approach has potential in solving complex control problems.

27 citations


Cited by
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Journal ArticleDOI
01 Apr 2012
TL;DR: In this review, the application of genetic algorithms, particle swarm optimization and ant colony optimization are considered as three different paradigms that help in the design of optimal type-2 fuzzy controllers.
Abstract: A review of the methods used in the design of interval type-2 fuzzy controllers has been considered in this work. The fundamental focus of the work is based on the basic reasons for optimizing type-2 fuzzy controllers for different areas of application. Recently, bio-inspired methods have emerged as powerful optimization algorithms for solving complex problems. In the case of designing type-2 fuzzy controllers for particular applications, the use of bio-inspired optimization methods have helped in the complex task of finding the appropriate parameter values and structure of the fuzzy systems. In this review, we consider the application of genetic algorithms, particle swarm optimization and ant colony optimization as three different paradigms that help in the design of optimal type-2 fuzzy controllers. We also mention alternative approaches to designing type-2 fuzzy controllers without optimization techniques. We also provide a comparison of the different optimization methods for the case of designing type-2 fuzzy controllers.

301 citations

Journal ArticleDOI
TL;DR: An overview of multiobjective evolutionary fuzzy systems is presented, describing the main contributions on this field and providing a two-level taxonomy of the existing proposals, in order to outline a well-established framework that could help researchers who work on significant further developments.
Abstract: Over the past few decades, fuzzy systems have been widely used in several application fields, thanks to their ability to model complex systems. The design of fuzzy systems has been successfully performed by applying evolutionary and, in particular, genetic algorithms, and recently, this approach has been extended by using multiobjective evolutionary algorithms, which can consider multiple conflicting objectives, instead of a single one. The hybridization between multiobjective evolutionary algorithms and fuzzy systems is currently known as multiobjective evolutionary fuzzy systems. This paper presents an overview of multiobjective evolutionary fuzzy systems, describing the main contributions on this field and providing a two-level taxonomy of the existing proposals, in order to outline a well-established framework that could help researchers who work on significant further developments. Finally, some considerations of recent trends and potential research directions are presented.

271 citations

Journal ArticleDOI
TL;DR: In this review, the application of genetic algorithms, particle swarm optimization and ant colony optimization are considered as three different paradigms that help in the design of optimal type-2 fuzzy controllers.

250 citations

Journal ArticleDOI
TL;DR: An optimization method is used to efficiently design the generalized type-2 fuzzy system to improve the control performance and the proposed method for control is applied to a non-linear control problem to test the advantages of the proposed approach.

207 citations