Title

Nonlinear System Modeling using Neural Networks

Abstract

Artificial neural networks have gained increasing popularity in control area in recent years. This paper outlines the application of a neural network based identification approach to a dynamical nonlinear system, a cantilever plate with distributed actuators and sensors. The type of neural networks utilized are multi-layer perceptrons with the backpropagation (BP) learning method. The identifier is implemented in discrete-time domain, and its performance is compared with a linear model from a previous result, that used frequency domain method. The time-domain neural network approach displays better nonlinear dynamical properties. A new efficient scheme to train the BP neural networks with a large amount of data is also introduced.

Meeting Name

Artificial Neural Networks in Engineering Conference, ANNIE '97 (1997: Nov. 9-12, St. Louis, MO)

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Actuators; Backpropagation; Computer Simulation; Identification (Control Systems); Learning Systems; Multilayer Neural Networks; Sensors; Time Domain Analysis; Backpropagation Learning Method; Dynamical Systems; Nonlinear Control Systems

International Standard Book Number (ISBN)

978-0791800645

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 1997 American Society of Mechanical Engineers (ASME), All rights reserved.

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