A Hybrid of Cooperative Particle Swarm Optimization and Cultural Algorithm for Neural Fuzzy Networks and Its Prediction Applications

This study presents an evolutionary neural fuzzy network, designed using the functional-link-based neural fuzzy network (FLNFN) and a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of cooperative particle swarm optimization and cultural algorithm....

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Bibliographic Details
Published in:IEEE transactions on human-machine systems 2009-01, Vol.39 (1), p.55-68
Main Authors: LIN, Cheng-Jian, CHEN, Cheng-Hung, LIN, Chin-Teng
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial intelligence
Artificial neural networks
Chaotic time series
Computer science
control theory
systems
Councils
cultural algorithm
Cultural differences
Evolutionary
Evolutionary algorithms
Exact sciences and technology
functional-link network
Fuzzy neural networks
Fuzzy sets
Fuzzy systems
Input variables
Mathematical models
Multi-layer neural network
neural fuzzy network
Neural networks
Optimization
Particle swarm optimization
Polynomials
prediction
Predictive models
Studies
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Summary:This study presents an evolutionary neural fuzzy network, designed using the functional-link-based neural fuzzy network (FLNFN) and a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of cooperative particle swarm optimization and cultural algorithm. It is thus called cultural cooperative particle swarm optimization (CCPSO). The proposed CCPSO method, which uses cooperative behavior among multiple swarms, can increase the global search capacity using the belief space. Cooperative behavior involves a collection of multiple swarms that interact by exchanging information to solve a problem. The belief space is the information repository in which the individuals can store their experiences such that other individuals can learn from them indirectly. The proposed FLNFN model uses functional link neural networks as the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the functional link neural networks. The FLNFN model can generate the consequent part of a nonlinear combination of input variables. Finally, the proposed FLNFN with CCPSO (FLNFN-CCPSO) is adopted in several predictive applications. Experimental results have demonstrated that the proposed CCPSO method performs well in predicting the time series problems.
ISSN:1094-6977
2168-2291
1558-2442
2168-2305
DOI:10.1109/TSMCC.2008.2002333