Title :
Noise-Tuning-Based Hysteretic Noisy Chaotic Neural Network for Broadcast Scheduling Problem in Wireless Multihop Networks
Author :
Ming Sun ; Yaoqun Xu ; Xuefeng Dai ; Yuan Guo
Author_Institution :
Coll. of Comput. & Control Eng., Qiqihar Univ., Qiqihar, China
Abstract :
Compared with noisy chaotic neural networks (NCNNs), hysteretic noisy chaotic neural networks (HNCNNs) are more likely to exhibit better optimization performance at higher noise levels, but behave worse at lower noise levels. In order to improve the optimization performance of HNCNNs, this paper presents a novel noise-tuning-based hysteretic noisy chaotic neural network (NHNCNN). Using a noise tuning factor to modulate the level of stochastic noises, the proposed NHNCNN not only balances stochastic wandering and chaotic searching, but also exhibits stronger hysteretic dynamics, thereby improving the optimization performance at both lower and higher noise levels. The aim of the broadcast scheduling problem (BSP) in wireless multihop networks (WMNs) is to design an optimal time-division multiple-access frame structure with minimal frame length and maximal channel utilization. A gradual NHNCNN (G-NHNCNN), which combines the NHNCNN with the gradual expansion scheme, is applied to solve BSP in WMNs to demonstrate the performance of the NHNCNN. Simulation results show that the proposed NHNCNN has a larger probability of finding better solutions compared to both the NCNN and the HNCNN regardless of whether noise amplitudes are lower or higher.
Keywords :
broadcast communication; chaotic communication; neural nets; optimisation; scheduling; telecommunication channels; telecommunication computing; time division multiplexing; BSP; broadcast scheduling problem; chaotic searching; gradual NHNCNN; hysteretic noisy chaotic neural networks; maximal channel utilization; noise amplitudes; noise-tuning-based hysteretic noisy chaotic neural network; optimization performance; stochastic noises; stochastic wandering; time-division multiple-access frame structure; wireless multihop networks; Neural networks; Neurons; Noise; Noise level; Optimization; Stochastic processes; Tuning; Broadcast scheduling problem; hysteresis; noise tuning; noisy chaotic neural network; wireless multihop networks;
Journal_Title :
Neural Networks and Learning Systems, IEEE Transactions on
DOI :
10.1109/TNNLS.2012.2218126