Chung Wen Hung, Wei Lung Mao, Han Yi Huang
Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 329-341, 2019, DOI:10.31209/2019.100000093
Abstract 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… More >
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