Automatic construction of feedforward/recurrent fuzzy systems by clustering-aided simplex particle swarm optimization

Juang, Chia‐Feng; Chung, I‐Fang; Hsu, Chao-Hsin

doi:10.1016/j.fss.2007.04.009

Cited by 31 publications

(8 citation statements)

References 26 publications

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“…In this respect, nonlinearity represents a challenge for feasibility because it is more difficult to reflect simultaneous nonlinear influence of the inputs on the output [21,45,59,82,102,124,135,138]. In this respect, nonlinearity represents a challenge for feasibility because it is more difficult to reflect simultaneous nonlinear influence of the inputs on the output [21,45,59,82,102,124,135,138].…”

Section: Comparison Of Fuzzy Systemsmentioning

confidence: 99%

Fuzzy Networks for Complex Systems

Gegov

2011

Studies in Fuzziness and Soft Computing

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Section: Comparison Of Fuzzy Systemsmentioning

confidence: 99%

Fuzzy Networks for Complex Systems

Gegov

2011

Studies in Fuzziness and Soft Computing

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“…Particle swarm optimization (PSO), a novel evolutionary algorithm paradigm, 17,18 has been proposed to tackle the fuzzy modeling problems in recent literatures. [19][20][21] Particle swarm optimization is a population-based stochastic optimization algorithm introduced by Eberhart and Kennedy 17 and Kennedy and Eberhart. 18 Recently, various attempts have been made to improve the performance of the original PSO algorithm for the identification of the parameters of the local linear TS fuzzy models, such as chaos PSO (CPSO), 22 Euclidean PSO, 23 adaptive PSO (APSO), 24 and multiobjective fuzzy optimization.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy C‐regression model algorithm using a new PSO algorithm

Adel

Soltani

Châari

2017

Adaptive Control & Signal

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SummaryIn this paper, a new methodology is introduced for the identification of the parameters of the multiple-input-multiple-output local linear Takagi-Sugeno fuzzy models using the weighted recursive least squares (WRLS). The WRLS is sensitive to initialization, which leads to no convergence. In order to overcome this problem, adaptive chaos particle swarm optimization is proposed to optimize the initial states of WRLS. This new algorithm is improved versions of the original particle swarm optimization algorithm. Finally, comparative experiments are designed to verify the validity of the proposed clustering algorithm and the Takagi-Sugeno fuzzy model identification method, and the results show that the new method is effective in describing a complicated nonlinear system with significantly high accuracies compared with approaches in the literature. KEYWORDSchaos adaptive particle swarm optimization, fuzzy C-regression model clustering algorithm, identification, multiple-input multiple-output, Takagi-Sugeno fuzzy models

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“…The training problem of the neuro-fuzzy architecture has been configured as a highly multidimensional stochastic global optimization problem and improved variants of particle swarm optimization (PSO) techniques have been successfully implemented for it. Juang et al [9] proposed a new approach for automating the structure and parameter learning of fuzzy systems by clustering-aided simplex particle swarm optimization, called CSPSO. Once a new rule is generated, the corresponding parameters are further tuned by the hybrid of the simplex method and particle swarm optimization (PSO).…”

Section: Introductionmentioning

confidence: 99%

Research on a New Hybrid Optimization Algorithm based on QPSO and FNN

Li¹,

Zhang²,

Wang³

2016

IJSH

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Fuzzy neural network(FNN) is a neural network based on combining the advantages of the fuzzy theory and neural network. It has the characteristics of dealing with the nonlinear and fuzziness and so on. Particle swarm optimization(PSO) algorithm is a population-based search algorithm by simulating the social behavior of birds within a flock. So the quantum PSO(QPSO) algorithm is proposed for optimizing the parameters of FNN in order to construct a new hybrid optimization(QPSO-FNN) algorithm in this paper. In the proposed QPSO-FNN algorithm, the quantum theory is used to improve the PSO algorithm, then the global optimization ability of QPSO algorithm is optimize the parameters of FNN model by putting these parameters in the particle encoding. The found optimal values are regarded as the parameters of FNN model to obtain the final QPSO-FNN method. Finally, the QPSO-FNN algorithm is used to solve the complex problem, the experimental results show that the QPSO-FNN algorithm takes on the shorter response time and higher solving accuracy.

show abstract

Automatic construction of feedforward/recurrent fuzzy systems by clustering-aided simplex particle swarm optimization

Cited by 31 publications

References 26 publications

Fuzzy Networks for Complex Systems

Fuzzy Networks for Complex Systems

A fuzzy C‐regression model algorithm using a new PSO algorithm

Research on a New Hybrid Optimization Algorithm based on QPSO and FNN

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