@article {IOPORT.05010336,
    author       = {Yao, Yu and Gao, Fuxiang and Yu, Ge},
    title        = {Dissipative chaotic neuron and its time-delay classification.},
    year         = {2004},
    journal      = {Journal of Northeastern University. Natural Science},
    volume       = {25},
    number       = {9},
    issn         = {1005-3026},
    pages        = {825-828},
    publisher    = {Editorial Department of Journal of Northeastern University, Nanhu, Shenyang},
    abstract     = {Summary: The dynamics of a discrete and dissipative nonlinear model neuron is discussed. Numerical simulations demonstrate that the self-inhibitory units with nonzero decay rates exhihit 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 computatioml capability of BP neural network and provide a new type of classifying method. The neural network proposed can be generalized to other time-delay classification.},
    identifier   = {05010336},
}