利用自适应最优加权随机汇池网络对随机参数估计进行了理论和实验分析。将随机噪声优化的随机汇池网输出向量进行最优线性加权优化,给出了具有任意节点数目的随机汇池网络最优权向量以及估计信号与真实信号之间均方误差表达式。同时,在实际信号处理任务中,待估参数和噪声的统计信息经常是未知的,本文给出了基于观测数据的最优权向量和均方误差近似估计算法。理论和实验都验证了随机噪声对于随机汇池网络的优化能力,也展现了自适应最优加权随机汇池网络良好的估计性能。
景文腾
,
韩博
,
耿金花
,
许丽艳
,
段法兵
,
景文腾
,
韩博
,
耿金花
,
许丽艳
,
段法兵
. 最优加权随机汇池网络的估计性能研究[J]. 复杂系统与复杂性科学, 2018
, 15(3)
: 89
-93
.
DOI: 10.13306/j.1672-3813.2018.03.011
In this paper, the optimally weighted stochastic pooling network is investigated for the theoretical and experimental analyses of the random parameter estimation. The stochastic pooling network is first optimized by the random noise components, and then improved by the linear optimum weight coefficients. The theoretical expressions of the optimum weight vector and the mean square error of the stochastic pooling network with an arbitrary number of nodes are deduced. In practice, since the statistical information of parameter and background noise is often unknown, the approximation estimation algorithms of the optimum weight vector and the mean square error are presented and based on the observations. Theoretical and experimental results both verify the optimization ability of random noise, and show the outstanding estimation performance of the optimally weighted stochastic pooling network.
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