Application Of Improved Operators Genetic Algorithms In Parameters Learning Of Fuzzy Neural Network

The Improved Operators Genetic Algorithm (IOGA)is introduced. The original operations of crossing and variation are replaced with a simple operation of GA itself. Two parameters, i.e.cross-probability and variation-probability, are omitted here to avoid unreasonable sampling of P-c and P-M and the probability reduction of the chromosome of individual with high degree of adaptability. Finally, the improved algorithm is applied to parameters learning of fuzzy neural network(FNN)by forming a FNN controller. The simulation shows that it is effective and applicable. The(IOGA)shows a strong capacity of overall optimization and provides a good solution for complex nonlinear and combinatorial optimization problem.