書誌事項
- タイトル別名
-
- Identification of Nonlinear Continuous Systems by Using Neural Network Compensator
- ニューラル ネットワーク ゴサ ホショウキ オ モチイタ ヒセンケイ レンゾク
この論文をさがす
抄録
Most of real systems are continuous systems with nonlinearity. Usually, the systems are approximated by linear models, and are analyzed and designed using the well established linear system theory, because the analysis and the design of nonlinear systems are difficult, and the theories for them are not well established. However, there must be an approximation error due to the nonlinearity. In this paper, an approach to identification of nonlinear continuous time systems with measurement noise is proposed. In the approach the parameters of the linear approximate model are estimated from the sampled input-output data of the nonlinear systems by a low-pass filtering method, and the modeling error due to the nonlinearity is compensated by using neural network. Two types of neural network compensators are obtained based on two different ways of approximating the noise removed system output. In the training of the neural network, the teaching signals are provided by data smoothing method which enables on-line noise filtering and thus on-line training. The trained network compensats the modeling error effectively. An illustrative example is given to demonstrate the effectiveness of the proposed approach.
収録刊行物
-
- 電気学会論文誌C(電子・情報・システム部門誌)
-
電気学会論文誌C(電子・情報・システム部門誌) 114 (5), 595-602, 1994
一般社団法人 電気学会
- Tweet
キーワード
詳細情報 詳細情報について
-
- CRID
- 1390001204608691968
-
- NII論文ID
- 130006844385
-
- NII書誌ID
- AN10065950
-
- ISSN
- 13488155
- 03854221
-
- NDL書誌ID
- 3873154
-
- データソース種別
-
- JaLC
- NDL
- Crossref
- CiNii Articles
-
- 抄録ライセンスフラグ
- 使用不可