Abstract：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.
韩博, 刘佳, 耿金花, 段法兵. 加权随机汇池网络中递归最小二乘算法研究[J]. 复杂系统与复杂性科学, 2020, 17(1): 81-86.
HAN Bo, LIU Jia, GENG Jinhua, DUAN Fabing. Study of Recursive Least Square Adaptive Algorithm for Weighted Stochastic Pooling Networks. Complex Systems and Complexity Science, 2020, 17(1): 81-86.
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