@Article{2019.100000093,
AUTHOR = {Chung Wen Hung, Wei Lung Mao, Han Yi Huang},
TITLE = {Modified PSO Algorithm on Recurrent Fuzzy Neural Network for System  Identification},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {25},
YEAR = {2019},
NUMBER = {2},
PAGES = {329--341},
URL = {http://www.techscience.com/iasc/v25n2/39660},
ISSN = {2326-005X},
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 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).},
DOI = {10.31209/2019.100000093}
}