Parameter Optimization and Simulation for Fuzzy Tree Model Based on Genetic Algorithm
Mao Jianqin · Acta Simulata Systematica Sinica · 2002
Improved from back propagation of fuzzy tree model, a kind of Takagi-Sugeno fuzzy model, this paper proposes a new parameter optimization method based on genetic algorithm (GA). Two keys are to select the way of determination for parent nodes?a and to select what variables as genes on chromosome. Benchmark simulations illustrate that the method have less sensitization for initial parameters and well evolution efficiency. On the same precise level, new method can simplify partition of data space and decrease computational load.