面向信道分配的分段退火混沌神经网络模型

Stepped annealing chaotic neural network model for the channel assignment problem

基于暂态混沌神经网络退火过程分段的思想,改进了一种混沌神经网络模型。根据所对应的Lyapunov指数变化来确定模型的分段点,使网络既能有效利用混沌态进行全局搜索,又能加快收敛速率;基于移动通信系统的信道分配问题,针对不同规模进行了仿真分析与研究,并运用Kunz基准测试程序面向25小区进行了模型测试与分析。仿真实验结果表明,较之其他混沌神经网络模型,本模型有效减少了网络运算的迭代步数,提高了网络的搜索效率,在较大网络规模下显示出更为优良的性能。

Based on the stepped annealing method used in transient chaotic neural network, a chaotic neural network model had been improved. The dividing point in the new model was chosen according to the change of the corresponding Lyapunov exponent, and made sure the network take good advantage of the property of chaos to search the global minimum and accelerate the convergence process. Various scales of the channel assignment problems had been simulated and researched based on channel assignment problem in mobile communication system. Kunz benchmark test problem was also used for the emulation of the channel assignment problem based on 25-district model. Simulation results show that compared with other transient chaotic neural networks, the new stepped annealing method decreases the computing time and enhances the searching ability, and has a better performance in the larger scale of network.