Nonlinear System Modeling using Neural Networks
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.
T. Han et al., "Nonlinear System Modeling using Neural Networks," Intelligent Engineering Systems Through Artificial Neural Networks, vol. 7, pp. 601 - 606, American Society of Mechanical Engineers (ASME), Nov 1997.
Artificial Neural Networks in Engineering Conference, ANNIE '97 (1997: Nov. 9-12, St. Louis, MO)
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)
Article - Conference proceedings
© 1997 American Society of Mechanical Engineers (ASME), All rights reserved.
01 Nov 1997