A novel hysteretic noisy chaotic neural network and its application to TDMA broadcast scheduling

Abstract

In order to enhance the optimization ability of hysteretic dynamics in the noisy chaotic neural network, and not to increase any parameters into the noisy chaotic neural network, this paper presents a novel hysteretic noisy chaotic neural network by taking noise amplitudes of the noisy chaotic neural network as center parameters of Sigmoid function and using inputs' change of neurons to control noise amplitudes to form hysteretic loop. The proposed network can evolve dynamics including chaotic reverse bifurcation, stochastic wandering and hysteresis. Simulations in TDMA broadcast scheduling problem in packet radio networks suggest that the proposed hysteretic noisy chaotic neural network can behave better optimization performance.