Chaotic Neural Network with White Noise for Broadcast Scheduling Problems in Packet Radio Networks
Transient chaotic neural network (TCNN) with chaotic simulated annealing (CSA), which can control the state searching in a fractal region, has a higher searching efficiency. However, the TCNN is not guaranteed to find the globally optimal solutions because CSA has completely deterministic dynamics. Contrary to CSA, stochastic simulated annealing (SSA) can search a globally optimal solution with probability one if the annealing speed is sufficiently slow. The white noise is one of the most common stochastic model and used widely in the engineering. In order to retain the excellent optimization property of SSA, the white noise is proposed into the TCNN. And the improved neural network is applied in the broadcast scheduling problems (BSP). The simulation results show that as long as we control the white noise in a proper range, we will be more likely to find an optimal or near-optimal time -division multiple-access (TDMA) frame structure with a minimal average delay time.
Stochastic simulated annealing White Noise Broadcast scheduling problems TDMA
Yaoqun XU Yulei LI
Institute of System Engineering, Harbin University of Commerce, Harbin, China
国际会议
2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)
四川绵阳
英文
3058-3061
2011-05-23（万方平台首次上网日期，不代表论文的发表时间）