一种耗散型混沌神经元及其延时分类

Dissipative Chaotic Neuron and Its Time-Delay Classification

讨论了离散的、耗散型非线性神经元模型动力学·数值模拟表明模型中带有非零衰减系数时,自抑制神经元呈现出复杂的动力学模式,其中包括倍周期分叉通往混沌·利用混沌神经元对BP网络结果进行后处理,组成BP/CNN混合神经网络,利用其倒分岔特性实现延时分类·构建的BP/CNN对典型的具有延时特性行为的SYNflooding滥用入侵进行检测,结果表明该混合神经网络具有灵活的延时分类能力,扩展了BP神经网络的计算能力,提供了一种新的分类处理方法,可以推广到识别其他延时分类的事件中·

The dynamics of a discrete and dissipative nonlinear model neuron is discussed. Numerical simulations demonstrate that the self-inhibitory units with non-zero decay rates exhibit a complex dynamics including period doubling routes to chaos. A BP/CNN hybrid neural network is constructed using the chaotic neuron in the neural network to conduct an after-processing for the output from BP network, with the reverse bifurcation of the chaotic neuron used to implement time-delay classification. The BP/CNN network thus constructed can detect the SYN flooding misuse intrusion featured with typical time-delay behavior. The result shows that these types of hybrid neural network have a capability for flexible time-delay classification so as to extend the computational capability of BP neural network and provide a new type of classifying method. The proposed neural network can be generalized to other time-delay classification.