三角函数自反馈混沌神经网络及其应用

Yao-qun Xu, Shaoping He
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引用次数: 1

摘要

通过引入非线性三角函数作为混沌神经网络的自反馈,提出了一种具有三角函数自反馈的混沌神经网络。给出了混沌神经元的逆分岔和最大Lyapunov指数。将该混沌神经网络应用于求解10城旅行商问题,分析了三角函数自反馈对TSP的影响。在TSP上的仿真结果表明,具有三角函数自反馈的混沌神经网络能够以更快的速度实现全局优化,并且所提出的混沌神经网络的优化性能优于具有线性自反馈的混沌神经网络。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Chaotic neural network with self-feedback of trigonometric function and its application
A chaotic neural network with self-feedback of trigonometric function is presented by introducing non-linear trigonometric function as self-feedback of chaotic neural network. The reversed bifurcation and the maximum Lyapunov exponent of the chaotic neuron are given. This chaotic neural network is used to the 10-city traveling salesman problem (TSP), and the influence of trigonometric function self-feedback on TSP is analyzed. The simulation on TSP indicates that the chaotic neural network with self-feedback of trigonometric function can realize the global optimization with faster speed and that the optimization performance of the proposed chaotic neural network is superior to that of the network with linear self-feedback.
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