| ID | 30131 |
| FullText URL | |
| Author |
Tanaka, Masahiro
|
| 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
|
| Note | Digital Object Identifier: 10.1109/CDC.1996.574577
Published with permission from the copyright holder. This is the institute's copy, as published in Decision and Control, 1996., Proceedings of the 35th IEEE, 11-13 Dec. 1996, Vol. 1, Pages 933-934. Publisher URL:http://dx.doi.org/10.1109/CDC.1996.574577 Copyright © 1996 IEEE. All rights reserved. |
| Published Date | 1996-12
|
| Publication Title |
Decision and Control
|
| Volume | volume1
|
| Start Page | 933
|
| End Page | 934
|
| Content Type |
Journal Article
|
| language |
English
|
| Refereed |
True
|
| DOI | |
| Submission Path | industrial_engineering/52
|
