小世界人工神经网络模型及其应用研究  

Model of NW Multilayer Feedforward Small-World Artificial Neural Networks and It's Applied

作　　者：张瑞成[1] 李冲[1] 

机构地区：[1]河北理工大学计算机与自动控制学院,河北唐山063009

出　　处：《计算机仿真》2011年第10期95-98,150,共5页Computer Simulation

基　　金：河北省自然科学基金资助项目(F2010000972)

摘　　要：关于优化神经网络模型的快速性和精度,为了寻找最优的神经网络结构,在复杂网络的研究方法对多层前向神经网络模型的基础上,提出一种在层次结构上处于规则型到随机型神经网络过渡的中间网络模型-NW型多层前向小世界人工神经网络模型。利用对多层前向规则神经网络中神经元以某一概率p随机化向后层跨层连接,构建新的神经网络模型,然后将不同跨层概率下的小世界人工神经网络应用于函数逼近。在设定精度相同情况下对不同概率下的收敛次数做比较,仿真发现随机化加边概率p处于p=0.08附近时的小世界人工神经网络比同规模的规则网络和随机网络具有更好的收敛速度,实验证实采用NW型小世界多层前向人工神经网络模型,在精度和收敛速度上均得到提高。To find the optimal neural network structure,based on the research methods from the complex network,the structure of multi-layer forward neural networks model was studied,and a new neural networks model,NW multi-layer forward small world artificial neural networks was proposed,whose structure of layer was between the regular model and the stochastic model.At first,the regular of multilayer feed-forward neural network neurons randomized cross-layer link back layer with a probability p,and constructed the new neural network model.Secondly,the cross-layer small world artificial neural networks were used for function approximation under different re-wiring probability.The count of convergence under different probability was compared by setting a same precision.Simulation shows that the small-world neural network has a better convergence speed than regular network and random network nearly p=0.08,and the optimum performance of the NW multi-layer forward small world artificial neural network is proved in the right side of probability increases.

关 键 词：小世界网络 多层前向神经网络 函数逼近 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]