A Gaussian Function Based Chaotic Neural Network

摘要：

In this paper we choose the non-monotonic Gaussian function as activation function of the recurrent neural network to built a Gaussian function based chaotic neural network. The discrete dynamics of this network are discussed to find the proper network parameters, such as weight, bias and input. Numerical simulations demonstrate that this network can exhibit period doubling bifurcations from stationary states to stable period-2 orbits, and even the routes to chaos over certain parameter domains. The parameterized Gaussian function as an iterated map presents abundant dynamic behavior and its application in chaotic neural network may help to improve the global searching ability of the optimization problem.

关键词： chaotic neural network gaussian function dynamics bifurcation neuron

作者: Zuohan Zhou Weifeng Shi Yan Bao Ming Yang

作者单位: Department of Electrical Engineering and Automation Shanghai Maritime University Shanghai, P.R.China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 203-206

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）