Self-adaptive Control Based on Fuzzy Neural Network Using Genetic Algorithm

The artificial neural networks can approximate any non-linear function with any given precision.It has a high self-adaptability and self-organization,which endows the neural networks with large potential to solve the control of the system with high nonlinearity and serious uncertainty.Meanwhile,the currently used training algorithm for the neural networks such as BP is often inclined to local minimum,affected greatly by the initialization of the weights and has a low convergence velocity.An indirect self-adaptive fuzzy-neural network controller(FNNC) has been presented with its parameters and the structure tuned simultaneously by GA.The structure of the controller is based on the radical basis function (RBF) neural network with Gaussian membership functions.Dynamic crossover,mutation probabilistic rates as well as modified fitness function have been used for faster convergence.Flexible BP algorithm has been applied for neural network identification off-line of system forward model.Simulation results show that the FNNC presents encouraging advantages.