Speech recognition based on cooperative particle swarm optimizer wavelet neural network

In BP wavelet neural network, the learning algorithm is BP algorithm, it is the stochastic gradient algorithm virtually, and it is local search algorithm, using this algorithm, the network may get into local minimum, the result of network training is dissatisfactory. In this paper, the cooperative Particle Swarm Optimizer algorithm CPSO) being used to train the parameters of the Wavelet Neural Network. The CPSO is a variant of the Particle Swarm Optimizer (PSO) that splits the problem vector; for example a neural network weight vector; across several swarms. This paper investigates the influence that the number of swarms used (also called the split factor) has on the training performance of a wavelet neural network. Then the CPSO-WNN being used in noise speech recognition, simulation results show compared with the BP network, the iterative number, error of the function approximation and the performance of the network are highly improved than BP network, the recognition rate are highly improve also.