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

机译:一种新颖的迟滞噪声混沌神经网络及其在TDMA广播调度中的应用

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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.
机译:为了提高噪声混沌神经网络中滞后动力学的优化能力,又不增加噪声混沌神经网络中的参数,本文提出了一种新颖的滞后噪声混沌神经网络。作为Sigmoid函数的中心参数,并利用输入的神经元变化来控制噪声幅度以形成磁滞回线。所提出的网络可以发展动力学,包括混沌反向分叉,随机漂移和滞后。分组无线网络中的TDMA广播调度问题的仿真表明,所提出的滞回噪声混沌神经网络可以表现出更好的优化性能。

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