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Modified PSO Algorithm on Recurrent Fuzzy Neural Network for System Identification

Chung Wen Hung, Wei Lung Mao, Han Yi Huang

Graduate School of Engineering Science and Technology and Department of Electrical Engineering, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan

* Corresponding Author: Chung Wen Hung, email

Intelligent Automation & Soft Computing 2019, 25(2), 329-341.


Nonlinear system modeling and identification is the one of the most important areas in engineering problem. The paper presents the recurrent fuzzy neural network (RFNN) trained by modified particle swarm optimization (MPSO) methods for identifying the dynamic systems and chaotic observation prediction. The proposed MPSO algorithms mainly modify the calculation formulas of inertia weights. Two MPSOs, namely linear decreasing particle swarm optimization (LDPSO) and adaptive particle swarm optimization (APSO) are developed to enhance the convergence behavior in learning process. The RFNN uses MPSO based method to tune the parameters of the membership functions, and it uses gradient descent (GD) based scheme to optimize the parameters of the conclusion part of the fuzzy system. The effectiveness of our method is evaluated for three nonlinear system modelling and signal prediction, including Henon system, nonlinear plant system and Mackey-Glass time series. Simulation results show that the proposed RFNN with LDPSO algorithm can provide more effective and accurate identification performances compared with the APSO method in term of mean squared error (MSE).


Cite This Article

C. W. Hung, W. L. Mao and H. Y. Huang, "Modified pso algorithm on recurrent fuzzy neural network for system identification," Intelligent Automation & Soft Computing, vol. 25, no.2, pp. 329–341, 2019.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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