Title :
An optimal design approach for fuzzy systems based on hybrid genetic algorithms
Author :
Shuhong, Tong ; Yi, Shen ; Zhiyan, Liu
Author_Institution :
Harbin Inst. of Technol., China
fDate :
6/22/1905 12:00:00 AM
Abstract :
This paper proposes a hierarchical hybrid genetic algorithm (GA) based on an adaptive fuzzy-neural network with varying nodes. This algorithm extracts important rules from a given large rule base to construct an optimal fuzzy model using the GA, and parameters of the model are estimated using a hybrid of the gradient descent and least square estimate in terms of the characteristics of fuzzy systems. The hybrid GA combines the advantages of GA´s strong search capacity and the fast convergence and accuracy of the conventional optimization. Therefore, the algorithm achieves a trade-off between accuracy, reliability and computing time in global optimization. The simulation and application example given demonstrate its effectiveness
Keywords :
fuzzy neural nets; genetic algorithms; gradient methods; least squares approximations; parameter estimation; convergence; fuzzy model; fuzzy-neural network; genetic algorithm; gradient descent method; least square estimate; optimization; parameter estimation; Adaptive systems; Algorithm design and analysis; Fuzzy systems; Genetic algorithms; Least squares approximation;
Conference_Titel :
Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on
Print_ISBN :
0-7803-5995-X
DOI :
10.1109/WCICA.2000.860042