Genetic Algorithm And Its Application Study On Fuzzy Identification

Genetic algorithm is a searching algorithm that simulates biology evolvement in nature. Because of its briefness, strong robustness, especially it need not expert domain knowledge but fitness function as the value guide searching process, its applications are very abroad. Based on various domestic and overseas references, aiming at simple genetic algorithm's shortcomings, the dissertation brings forward two improved genetic algorithms, studies genetic algorithm's application on acquiring nonlinear equations' solutions, and applies genetic algorithm to fuzzy identification. The main results are as follows:Aiming at simple genetic algorithm's slow convergence and slow resolving power and so on, extended zooming genetic algorithms are brought forward, i.e. it keeps invariable string length, unceasingly leaves evolving information in decoding formula from individual string, and spare genes have function such as memory bank which is supplied with new genes for improving solution precision. Simultaneously, for preventing prematurity, crossover positions in crossover operator choose according to not equal probability. When bringing into new genes, reverse bit binary strings those have the same number as the optimal individuals are supplied, which settles gene absence problem reproduction results in.Using memory function of tabu search and searching, stochastic and disciplinary characteristics chaos optimization has, a hybrid genetic algorithm based on tabu search and chaos optimization is implemented to the population which passed through the genetic algorithm one time, which can induce the evolution of the population rapidly. This algorithm easily escapes from local optimal solution, have high searching efficiency, simple structure, convenient use.Aiming at iteration, optimization and MATLAB optimization toolbox having low precision and difficulty to choose initial vector on acquiring nonlinear equations' solutions, equations' solution problem is translated into genetic algorithm optimization problem. Nonlinear equations' usual genetic algorithm solution has been set up, and it has been used on automobile coasting test data processing.Genetic algorithm is used on fuzzy identification on nonlinear systems, based on fuzzy partition of input space, it adopts adaptive extended gauss function as member function, uses improved genetic algorithm to optimize its figure, uses the recursive least square method to identify the conclusion parameters of the fuzzy model. This method synthetizes genetic algorithm global searching ability and fast local searching ability of the recursive least square. Finally the effectiveness and practicability of this method is demonstrated by the simulation results example.