复合正弦函数混沌神经网络及优化计算应用

Study on model of chaotic neural network with sinusoidal function and application in optimization calculation

混沌神经网络已经成功地解决了函数优化和组合优化问题.通过复合正弦函数和S igmoid函数构成激励函数,构造了一种新的暂态混沌神经网络.给出了该神经元的倒分叉图和最大LE指数,分析了混沌动力学特性,并将其应用于函数优化问题.仿真结果表明,新的暂态混沌神经网络优于原来的混沌神经网络.

Chaotic neural network has successfully solved the function optimization and combinatorial optimization problem.This paper presents a new model of chaotic neural network whose activation function is composite of sinusoidal function and Sigmoid function.The reversed bifurcation and the maximum Lyapunov exponent of the chaotic neuron are given and the dynamic system is analyzed,and is used to function optimization and combinational optimization problems.The simulation results show that the searc-optimization capacity of chaotic neural network with sinusoidal Function is improved and the reformative chaotic neural network is superior to the primary chaotic neural networks.