Multilayer feedforward small-world neural networks and its function approximation
多层前向小世界神经网络及其函数逼近

Based on the research results from complex networks, a new neural networks model, multilayer feedforward small-world neural networks, is proposed, whose structure is between the regular and random connection model. At first, a new networks model is built up on rewiring the links of multilayer feedforward regular neural networks according to the rewiring probability p, and the characteristic parameters of new model show that it is different from the Watts-Strogatz model on clustering coefficients when 0 < p < 1. Secondly, the networks model is described as a six-element composition. Finally, when using multilayer feedforward small-world neural networks for function approximation under different p, the simulation results show that the networks have the best approximate performance when p = 0:1, and the comparison of convergent performance also shows that the small-world neural networks is superior to the same scale regular networks and random networks to a certain extent in convergence and approximate speed at the same rewiring probability.