OPTIMIZATION OF NEURO FUZZY MODEL FOR DATA TIME SERIES WITH SINGULAR VALUE DECOMPOSITION METHOD

Abstract

This study aims to develop new procedures in optimal neuro fuzzy modeling for time series data. Specifically in this research, the development of new procedure in modeling fuzzy Takagi-Sugeno-Kang order one for time series data which parameter-parametemic determination is done by singular value and neural network decomposition method, in order to obtain method of forming neuro fuzzy model for time series data optimal. In this research, we have developed a procedure to get the optimal Takagi-Sugeno-Kang fuzzy model for time series data by optimizing the parameter value search in consequence of fuzzy rule using singular value decomposition method. A new model of neuro fuzzy modeling is optimized, the fuzzy model whose parametem optimization is based on the neural network by the singular value decomposition method. Parameters in consequent part of the rule of fuzzy are optimized by the singular value decomposition method and the parameters in the antecedent part of the fuzzy rule are optimized based on neural network backpropagation with gradient descent method.