【摘要】 提出了一种新的混沌神经元模型,该神经元的激励函数由Gauss和Sigmoid两种函数加和组成的非单调函数构成.通过分岔图及Lyapunov指数的计算,分析了其动力学特性.基于该模型,构造了一种新的具有暂态混沌特性的神经网络,该网络在寻优过程中同时衰减两种退火机制实现混沌退火.通过将特征点匹配问题转化为优化问题,该网络可以实现对目标识别问题的求解.仿真实验验证了该算法的有用性和有效性.更多还原

【Abstract】 <Abstrcat>We give a novel chaotic neuron model whose activation function is composed of Gauss and Sigmoid function.It is shown that the model may exhibit a complex dynamic behavior.The most significant bifurcation processes,leading to chaos,are investigated through the computation of the Lyapunov exponents.Based on this neuron model,we propose a novel chaotic neural network,which realizes simulated chaotic anneal by decaying two parameters simultaneously.Transforming the feature points matching problem into the optimization problem,the network can complete the function of the object recognition.The simulation results prove the validity of the algorithm.更多还原