摘要
实际应用中信号和噪声的统计知识经常是未知的,为探究未知噪声环境中加权随机汇池网络模型的信号参数估计性能,本文研究了加权随机汇池网络中信号估计的递归最小二乘自适应递推算法,分析了该模型下算法的收敛性、均方误差、学习曲线等统计特征。在非平稳输入信号下,引入遗忘因子,探究了算法有效跟踪信号变化的能力。实验结果验证了关于算法收敛性与均方误差性能的理论分析,同时还证实了自适应过程中的超阈值随机共振现象。本文研究结果对于加权随机汇池网络的实际应用具有理论指导义。
In practice,the probability distribution model of the background noise is often unknown.Under this circumstance,a recursive least square adaptive algorithm is developed to estimate the random signal via the weighted stochastic pooling network.The analytical formula of the recursive least square adaptive algorithm is derived,and the convergence of the algorithm,the mean square error of the network outputs and the learning curve are analyzed.For non-stationary input signals,the proposed algorithm with the forgetting factor can effectively track the change of the signal.These theoretical results are demonstrated by the numerical experiments,and the phenomenon of suprathreshold stochastic resonance is also observed.The obtained results lay the fundamental framework for the application of the weighted stochastic pooling network in signal estimation.
作者
韩博
刘佳
耿金花
段法兵
HAN Bo;LIU Jia;GENG Jinhua;DUAN Fabing(Institute of Complexity Science,Qingdao University,Qingdao 266071,China)
出处
《复杂系统与复杂性科学》
EI
CSCD
2020年第1期81-86,共6页
Complex Systems and Complexity Science
基金
国家自然科学基金(61573202)。
关键词
随机汇池网络
递归最小二乘算法
收敛性
均方误差
非平稳信号
stochastic pooling network
recursive least square adaptive algorithm
convergence
mean square error
nonstationary signal
