一种基于改进暂态混沌神经网络的信道分配算法
doi: 10.3724/SP.J.1146.2006.01163
A New Algorithm Based on the Improved Transient Chaotic Neural Network for Cellular Channel Assignment
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摘要: 该文针对暂态混沌神经网络(TCNN)求解信道分配问题(CAP)，分析混沌神经网络模型及其混沌性态，依据其按自反馈连接权值的减小，由混沌态通过逆分岔而收敛到稳定状态的特性，提出了一种对暂态混沌神经网络进行分段退火的策略，即依据混沌神经网络运行过程中，对应Lyaponov指数的变化特性而确定分段点，使网络能有效地利用混沌态进行全局搜索和加快收敛；在7小区的信道分配中，网络收敛速度提升了30%左右，在25小区的Kunz基准测试程序的仿真中，收敛速度也提升了近15%；仿真结果表明其有效减少了网络运算的迭代步数，提高了网络的搜索效率；通过相应理论和仿真结果的分析，对网络的搜索性能、参数的选择与设置进行了进一步的讨论。Abstract: In this paper, the Transient Chaotic Neural Network(TCNN) is used to solve the Channel Assignment Problem(CAP), and a new method named two-stage annealing method in TCNN is proposed. The neural network gradually convergences, through the transient chaos, to a stable equilibrium point according to the damping of the self-feedback connection weight, and the dividing point in the new model is chosen according to the change of the corresponding Lyapunov exponent. The two- stage annealing method can make sure the network take good advantage of the chaos to search the global minimum and enhance the convergence rate. In the 7-cell cellular network, the convergence rate is 30% higher than the TCNN model，and is also upgraded 15% in the Kunzs benchmark test. Simulated results show that the new model has a higher searching ability and lower computing time in searching the global minimum. The searching ability and the choosing of the parameters are also discussed based on the simulated results.
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