Identification of nonlinear systems with missing data using stochastic neural network

被引：0
|
作者：
Tanaka, M
机构：
来源：
PROCEEDINGS OF THE 35TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-4 | 1996年
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, nonlinear identification is dealt with by using Gaussian sum distribution. Recently this model is called stochastic neural networks. By using the stochastic model, it is possible to estimate the output and also the missing elements in the input vector within the framework of conditional estimation. The model parameters can be estimated by using the EM algorithm. By interpolating the unknown elements, we don't have to discard the vectors including the missing elements.
引用
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页码：933 / 934
页数：2
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