摘要
提出一种具有暂态混沌的细胞神经网络,该网络是利用欧拉算法将模型的状态方程转化为离散形式并引入一项负的自反馈而形成的.由对单个神经元的仿真发现,该模型具有分叉和混沌的特性.在函数优化中,该网络首先经过一个倍周期倒分叉过程进行混沌搜索;然后进行类似Hopfield网络的梯度搜索.由于该网络利用了混沌搜索固有的随机性和轨道遍历性,因而具有较强的全局寻优的能力.最后通过2个函数优化的例子验证了该网络的有效性.
A model of cellular neural network with transient chaos is proposed,in which a negative self-feedback is introduced into a cellular neural network after transforming the dynamic equation to discrete time via Euler＇s method.The simulation of single neuron models shows the characteristics of bifurcation and chaos.In optimization,the model gradually approaches,through a chaos search by the course of reversed period-doubling bifurcations,to a dynamical structure similar to the Hopfield neural network which converges to a stable equilibrium point.As the model has rich dynamics such as randomicity,it can be expected to have robust search ability for global optimal solutions.Finally,simulation results on two examples of function optimization show the effectiveness of the neural networks.
出处
《控制与决策》
EI
CSCD
北大核心
2010年第5期691-694,共4页
Control and Decision
基金
国家自然科学基金项目(60672128)
关键词
细胞神经网络
分叉
混沌
函数优化
Cellular neural networks
Bifurcation
Chaos
Function optimization
