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http://dx.doi.org/10.7236/JIWIT.2011.11.4.091

A Study on the Prediction of the Nonlinear Chaotic Time Series Using Genetic Algorithm based Fuzzy Neural Network  

Park, In-Kyu (중부대학교 컴퓨터학과)
Publication Information
The Journal of the Institute of Internet, Broadcasting and Communication / v.11, no.4, 2011 , pp. 91-97 More about this Journal
Abstract
In this paper we present an approach to the structure identification based on genetic algorithm and to the parameter identification by hybrid learning method in neuro-fuzzy-genetic hybrid system in order to predicate the Mackey-Glass Chaotic time series. In this scheme the basic idea consists of two steps. One is the construction of a fuzzy rule base for the partitioned input space via genetic algorithm, the other is the corresponding parameters of the fuzzy control rules adapted by the backpropagation algorithm. In an attempt to test the performance the proposed system, three patterns, x(t-3), x(t-6) and x(t-9), was prepared according to time interval. It was through lots of simulation proved that the initial small error of learning owed to the good structural identification via genetic algorithm. The performance was showed in Table 2.
Keywords
Genetic Algorithm; Fuzzy Input Partition; Backpropagation Neural Networks;
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