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Modified PSO Algorithm on Recurrent Fuzzy Neural Network for System Identification



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).



Total Pages: 15


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Online Article


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present


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