A Soft Computing Based Approach for Modeling of Chaotic Time Series
Nonlinear dynamic time series modeling is a generic problem, which permeates all fields of science. The authors have developed a soft computing based methodology for the modeling of systems represented by such series. The soft computing techniques that are a consortium of emerging technologies, have recently provided an alternative approach to mathematical modeling. The implementation of soft computing is based on the exploitation of the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low cost solution. Fuzzy logic, neural networks and genetic algorithms are considered to be principal constituents of soft computing. Of these, the first component is primarily concerned with imprecision of data and information, the second with learning, and the third with optimization. In many applications, it is advantageous to exploit the synergism of these methods by using them in combination, rather than alone. The proposed model is based on Kasabov’s Evolving Fuzzy Neural Network and employs a genetic algorithm based method for the optimization of the most important parameters that govern the development of its structure. The well-examined Box Jenkins problem, to predict future values of the time series, based on the past history, is used as an illustrative example to demonstrate the potential of the proposed Genetic Evolving Fuzzy Neural Network (GEFuNN) model. The proposed methodology may find applications in the areas of signal processing, control, weather forecasting, economic and business planning and several other fields.
KeywordsGenetic Algorithm Membership Function Soft Computing Error Threshold Chaotic Time Series
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