Evolutionary design of RBF neural network based on multi-species cooperative particle swarm optimizer
基于多种群协同进化微粒群算法的径向基神经网络设计

Combination design of neural network's structure and weights has been one of the research focuses in neural network's evolutionary design.In this paper,a multi-species cooperative particle swarm optimizer is proposed by combining the ideas in the standard particle swarm optimization and hierarchy method.In the new algorithm,the individual free movement of particles within the species and the species population's movement evolve in a hierarchy model.The developed algorithm overcomes the limitation of particle's "prematurity" in global optimization using the standard PSO.When this algorithm is used in the training of RBF neural network's structure and parameters,the neural network shows a satisfactory accuracy and convergence in nonlinear system identification.The resulting network is able to properly balance the relation between generation and approximation accuracy.