Hierarchical Genetic Algorithms for Type-2 Fuzzy System Optimization Applied to Pattern Recognition and Fuzzy Control

Sánchez, Daniela; Melín, Patricia

doi:10.1007/978-3-319-05170-3_2

Cited by 4 publications

(3 citation statements)

References 22 publications

(14 reference statements)

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“…This characteristic of type-2 fuzzy sets makes type-2 fuzzy system especially useful to handle corrupted data processing, noise disturbance and knowledge uncertainty, as well as situations where shapes, positions or other parameters of membership functions are uncertain. Recently, type-2 fuzzy system has been used in many successful applications in various areas where uncertainties occur, such as in decision making [11,35], diagnostic medicine [5,38], signal processing [29,44], traffic forecasting [53], mobile robot control [4], pattern recognition [28,47,52], intelligent control [36,49].…”

Section: Advantages and Application Of Type-2 Fuzzy Logic Systemmentioning

confidence: 99%

An interval type-2 T-S fuzzy classification system based on PSO and SVM for gender recognition

Chen

et al. 2014

Multimed Tools Appl

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In this paper, an interval type-2 Takagi-Sugeno fuzzy classification system (IT2T-SFCS) learned by particle swarm optimization (PSO) and support vector machine (SVM) for antecedent and consequent parameters optimization is proposed. The IT2T-SFCS is constructed by fuzzy if-then rules whose antecedents are interval type-2 fuzzy sets and consequents are linear state equations. The antecedents of IT2T-SFCS use the fuzzy iterative self-organizing data analysis technique (ISODATA) and PSO to learn and calculate the optimal centers and the uncertain widths of the Gaussian membership functions. Consequent parameters in IT2T-SFCS are learned through SVM for the purpose of achieving higher generalization ability. The proposed IT2T-SFCS is able to directly handle uncertainties, minimize the effects of uncertainties and get the better generalization performance, which inherits the benefits of interval type-2 T-S fuzzy system and SVM. For demonstration, IT2T-SFCS is used as a classifier in gender recognition. The experimental results show that the performance of the proposed IT2T-SFCS is superior to that of the previous mainstream classifiers.

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Section: Advantages and Application Of Type-2 Fuzzy Logic Systemmentioning

confidence: 99%

An interval type-2 T-S fuzzy classification system based on PSO and SVM for gender recognition

Chen

et al. 2014

Multimed Tools Appl

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“…They are based on population coding and iterative search of the optimal value of a fitness function in this population. In case of FIS, researchers have focused on optimizing Membership Functions (MFs) [26] or fuzzy rules [27] or both of these simultaneously. The commonly used optimization methods include; Genetic Algorithms [27], Ant Colony Optimization (ACO) [17,24], Particle Swarm Optimization (PSO) [28], Bee Colony Optimization [29] and others [30].…”

Section: Introductionmentioning

confidence: 99%

“…In case of FIS, researchers have focused on optimizing Membership Functions (MFs) [26] or fuzzy rules [27] or both of these simultaneously. The commonly used optimization methods include; Genetic Algorithms [27], Ant Colony Optimization (ACO) [17,24], Particle Swarm Optimization (PSO) [28], Bee Colony Optimization [29] and others [30]. These algorithms have been applied for various UAV systems such as birotor helicopter [21,23,24], quadrotor [4,6,12,14,15,16,18,25], hexacopter [31] and so on.…”

Section: Introductionmentioning

confidence: 99%

Optimisation of fuzzy logic quadrotor attitude controller – particle swarm, cuckoo search and BAT algorithms

Zatout

Rezoug

et al. 2021

International Journal of Systems Science

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Bio-inspired optimization algorithms have recently attracted much attention in the control community. Most of these algorithms mimic particular behaviors of some animal species in such a way that allows solving optimization problems. The present paper aims at applying three metaheuristic methods for optimizing Fuzzy Logic Controllers used for quadrotor attitude stabilization. The investigated methods are Particle Swarm Optimization (PSO), BAT algorithm and Cuckoo Search (CS). These methods are applied to find the best output distribution of singleton membership functions of the Fuzzy Controllers. The quadrotor control requires measured responses, therefore, three objective functions are considered: Integral Squared Error, Integral Time-weighted Absolute Error and Integral Time-Squared Error. These metrics allow performance comparison of to compare the controllers in terms of tracking errors and speed of convergence. The simulation results indicate that BAT algorithm demonstrated higher performance than both PSO and CS. Furthermore, BAT algorithm is capable of offering 50% less computation time than CS and 10% less time than PSO. In terms of fitness, BAT algorithm achieved an average of 5% better fitness than PSO and 15% better than Cuckoo Search. According to these results, the BAT-based Fuzzy Controller exhibits superior performance compared with other algorithms to stabilize the quadrotor.

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Comparison of Optimization Techniques for Modular Neural Networks Applied to Human Recognition

Sánchez

Melín

Carpio

et al. 2016

Nature-Inspired Design of Hybrid Intelligent Systems

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Hierarchical Genetic Algorithms for Type-2 Fuzzy System Optimization Applied to Pattern Recognition and Fuzzy Control

Cited by 4 publications

References 22 publications

An interval type-2 T-S fuzzy classification system based on PSO and SVM for gender recognition

An interval type-2 T-S fuzzy classification system based on PSO and SVM for gender recognition

Optimisation of fuzzy logic quadrotor attitude controller – particle swarm, cuckoo search and BAT algorithms

Comparison of Optimization Techniques for Modular Neural Networks Applied to Human Recognition

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