Compensatory neural fuzzy network with symbiotic particle swarm optimization for temperature control

Peng, Chun‐Cheng; Chen, Cheng-Hung

doi:10.1016/j.apm.2014.05.040

Cited by 18 publications

(10 citation statements)

References 26 publications

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“…It is an effective way to improve the FNN performance by replacing the traditional learning algorithm by metaheuristic algorithms such as the particle swarm optimization (PSO) algorithm [13][14][15]. However, when the considered problem is a complex high-dimensional problem, PSO algorithm has the disadvantage of premature convergence [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Improved Chaotic Quantum-Behaved Particle Swarm Optimization Algorithm for Fuzzy Neural Network and Its Application

Peng

Lei

Yang

et al. 2020

Mathematical Problems in Engineering

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Traditional fuzzy neural network has certain drawbacks such as long computation time, slow convergence rate, and premature convergence. To overcome these disadvantages, an improved quantum-behaved particle swarm optimization algorithm is proposed as the learning algorithm. In this algorithm, a new chaotic search is introduced, and benchmark function experiments prove it outperforms the other five existing algorithms. Finally, the proposed algorithm is presented as the learning algorithm for Takagi-Sugeno fuzzy neural network to form a new neural network, and it is utilized in the water quality evaluation of Dongjiang Lake of Hunan province. Simulation results demonstrated the effectiveness of the new neural network.

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Section: Introductionmentioning

confidence: 99%

Improved Chaotic Quantum-Behaved Particle Swarm Optimization Algorithm for Fuzzy Neural Network and Its Application

Peng

Lei

Yang

et al. 2020

Mathematical Problems in Engineering

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“…In this regard, conventional FNN exhibits local, rather than global optimisation performance. Actually, an effective FNN should be able to not only adaptively adjust the network parameters but also dynamically optimise the fuzzy operations [22, 23]. Thus, a compensatory FNN (CFNN) with adaptive fuzzy operators was developed in [22].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, a compensatory FNN (CFNN) with adaptive fuzzy operators was developed in [22]. Therefore, CFNNs have recently been used in many successful applications such as control [23], modelling [24], and time series prediction [25]. In this study, it was utilised to construct an uncertainty observer regarding the non‐linearities of the VCA system.…”

Section: Introductionmentioning

confidence: 99%

Digital signal processor based intelligent fractional‐order sliding‐mode control for a linear voice coil actuator

Chen

Lee

2017

IET Control Theory & Applications

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“…The contribution is to develop a new intelligent particle swarm optimization (iPSO), where a fuzzy logic system developed based on human knowledge is proposed to determine the inertia weight for the swarm movement of the PSO and the control parameter of a newly introduced cross-mutated operation. Peng and Chen [31] proposed a symbiotic particle swarm optimization (SPSO) algorithm for compensatory neural fuzzy networks (CNFN). The CNFN model using compensatory fuzzy operators makes fuzzy logic systems more adaptive and effective.…”

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.

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Compensatory neural fuzzy network with symbiotic particle swarm optimization for temperature control

Cited by 18 publications

References 26 publications

Improved Chaotic Quantum-Behaved Particle Swarm Optimization Algorithm for Fuzzy Neural Network and Its Application

Improved Chaotic Quantum-Behaved Particle Swarm Optimization Algorithm for Fuzzy Neural Network and Its Application

Digital signal processor based intelligent fractional‐order sliding‐mode control for a linear voice coil actuator

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

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