Parameter Estimation in Nonlinear Systems Using Hopfield Neural Networks
Abstract
A method using the Hopfield neural network is developed for estimating the parameters of a nonlinear system whose theoretical model is assumed to exist. A linearization procedure is presented, and the errors between the dynamics of the plant and its model are minimized through a cost function that is equated to the energy function of a Hopfield neural network. The minimization process yields the weights and biases of the neural network. Proof of convergence of the modeled parameters to their true values and boundedness of parameter estimates at each step are provided. Numerical results from a scalar time-varying problem and a complex nine-state aircraft problem are presented to demonstrate the potential of this method.
Recommended Citation
Z. Hu and S. N. Balakrishnan, "Parameter Estimation in Nonlinear Systems Using Hopfield Neural Networks," AIAA Journal, American Institute of Aeronautics and Astronautics (AIAA), Feb 2005.
Department(s)
Mechanical and Aerospace Engineering
Keywords and Phrases
Hopfield Neural Networks; Parameter Estimation; Minimization Process; Nine State Aircraft Problem
International Standard Serial Number (ISSN)
000-11452
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2005 American Institute of Aeronautics and Astronautics (AIAA), All rights reserved.
Publication Date
01 Feb 2005
