J4 ›› 2006, Vol. 28 ›› Issue (3): 116-118.
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唐运虞 刘向东 修春波
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摘要:
本文提出了一种新颖的混沌神经元模型,其激励函数由Gauss函数和Sigmoid函数组成,分又图和Lyapunov指数的计算袁明其具有复杂的混沌动力学特性。在此基础上构成一种 暂态混沌神经网络,将大范围的倍周期倒分叉过程的混沌搜索和最优解邻域内的类似Hopfield网络的梯度搜索相结合,应用于函数优化计算问题的求解。实验证明,它具有较 较强的全局寻优能力和较快的收敛速度。
关键词: 暂态混沌 神经网络 函数优化
Abstract:
In this article a novel chaotic neural model whose activation function is composed ot Gaussuan and Sigrnoid functions is proposed. It is shown that the model may exhibit a complex and dynamic property. The most significant bifurcation processes,which lead to chaos, are investigated through the computation of the Lyapunov exponents. Based on this neural model, we propose a novel neural network with transient chaos. It can be applied to solving variouscomplicated optimization problems. Extensive numerical simulations show that the network has a higher ability of searching for globally optimal solutio ns and has a faster speed.
Key words: transient chaos, neural network, function optimization
唐运虞 刘向东 修春波. 一种新型暂态混沌神经网络及其在函数优化中的应用[J]. J4, 2006, 28(3): 116-118.
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http://joces.nudt.edu.cn/CN/Y2006/V28/I3/116