DocumentCode
307304
Title
Identification of nonlinear systems with missing data using stochastic neural network
Author
Tanaka, Masahiro
Author_Institution
Dept. of Inf. Technol., Okayama Univ., Japan
Volume
1
fYear
1996
fDate
11-13 Dec 1996
Firstpage
933
Abstract
In this paper, nonlinear identification is dealt with by using Gaussian sum distribution. This model is also called a stochastic neural network. 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
Keywords
Gaussian distribution; neural nets; nonlinear dynamical systems; parameter estimation; EM algorithm; Gaussian sum distribution; conditional estimation; missing data; nonlinear identification; nonlinear systems; stochastic neural network; Equations; Interpolation; Kernel; Neural networks; Nonlinear systems; Parameter estimation; Stochastic processes; Stochastic systems; Training data; Yield estimation;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 1996., Proceedings of the 35th IEEE Conference on
Conference_Location
Kobe
ISSN
0191-2216
Print_ISBN
0-7803-3590-2
Type
conf
DOI
10.1109/CDC.1996.574577
Filename
574577
Link To Document