Bayesian inference for neural network based high-precision modeling

被引：0

作者：

Morales, Jorge ^{[1
]}

Yu, Wen ^{[2
]}

机构：

[1] CINVESTAV IPN, Natl Polytech Inst, Dept Control Automat, Mexico City, DF, Mexico

[2] CINVESTAV IPN, Dept Control Automat, Mexico City, DF, Mexico

来源：

2022 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI) | 2022年

关键词：

neural networks; Bayesian inference; highprecision modeling; STOCHASTIC DISTRIBUTION; FAULT;

D O I：

10.1109/SSCI51031.2022.10022075

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we implement neural network structure and Bayesian inference in order to improve performance black-box modeling for unknonw nonlinear systems. This kind of structure works in batch form passing both the identification and the statistical training. Two nonlinear systems and two data sets of seismic information from regions of Italy and Mexico are used to evaluate the methods. The results are satisfied.

引用

页码：442 / 447

页数：6

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