Synchronous Control of Hysteretic Creep Chaotic Neural Network - Chunbo Xiu; Jianguo Hou; Yakun Zang; Guowei Xu; Chang Liu (IEEE Access, 2016)
A chaotic neuron with hysteretic and creep characteristics is proposed based on the conventional chaotic neuron model, with which a neural network is constructed, and the synchronous control between the hysteretic creep chaotic neuron or neural network and the deterministic chaotic neuron or neural network is investigated by the sliding mode control. The hysteretic activation function of the neuron is constructed by shifting the sigmoid function. The hysteretic parameters have creep properties, which lead to the uncertain responses of the neurons. The equivalent sliding mode control law can be designed according to the error equation of the synchronous system, and the Lyapunov method is used to prove the stability of the system. Furthermore, fuzzy sliding mode control law is designed to restrain the chattering, to reduce the synchronous error, as well as to shorten the transition time. The validity of this method has been proved by simulation experiments.