Noise Compensation Based Hysteretic Noisy Chaotic Neural Network for Broadcast Scheduling Problem in Wireless Multihop Networks

Abstract:

Compared with the noisy chaotic neural network, hysteretic noisy chaotic neural network always exhibits better optimization performance at higher noise levels, but exhibits worse optimization performance at lower noise levels. In order to enable the hysteretic noisy chaotic neural network to behave more excellent optimization performance not only at higher noise levels but also at lower noise levels, we introduce a noise compensation factor to the original hysteretic noisy chaotic neural network, and present noise compensation based hysteretic noisy chaotic neural network. The proposed network can outperform the hysteretic noisy chaotic neural network by the interaction of hysteretic activation function and the noise compensation factor. One benchmark broadcast scheduling problem is used to verify the superiority of the proposed network. The simulation results show that the proposed network takes advantages over the noisy chaotic neural network, the hysteretic noisy chaotic neural network and other algorithms.

• Periodical: Applied Mechanics and Materials (Volume 151)
• Pages: 527-531
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.151.527
• Online since: January 2012
• Authors: Ming Sun, Yuan Guo, Xue Feng Dai, Yao Qun Xu
• Keywords: Broadcast Scheduling Problem, Chaotic Neural Network, Hysteresis, Noise Compensation, Wireless Multihop Networks

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