Article
A Recursive Least Squares Training Algorithm for Multilayer Recurrent Neural Networks
Proceedings of the American Control Conference (1994, Baltimore, MD)
Abstract
Recurrent neural networks have the potential to perform significantly better than the commonly used feedforward neural networks due to their dynamical nature. However, they have received less attention because training algorithms/architectures have not been well developed. In this study, a recursive least squares algorithm to train recurrent neural networks with an arbitrary number of hidden layers is developed. The training algorithm is developed as an extension of the standard recursive estimation problem. Simulated results obtained for identification of the dynamics of a nonlinear dynamical system show promising results.
Meeting Name
American Control Conference (1994: Jun. 29-Jul. 1, Baltimore, MD)
Department(s)
Mechanical and Aerospace Engineering
Second Department
Computer Science
Sponsor(s)
AACC
Keywords and Phrases
- Feedforward Neural Networks,
- Learning (Artificial Intelligence),
- Least Squares Approximations,
- Multilayer Perceptrons,
- Multilayer Recurrent Neural Networks,
- Nonlinear Dynamical System,
- Recurrent Neural Nets,
- Recursive Estimation,
- Recursive Least Squares Training Algorithm,
- Training Architectures,
- Convergence of numerical methods,
- Identification (control systems),
- Learning systems,
- Least squares approximations,
- Neural networks,
- Nonlinear control systems,
- Recursive functions,
- Nonlinear dynamical systems,
- Algorithms
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 1994 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
6-1-1994
Publication Date
01 Jun 1994
Disciplines
Citation Information
Q. Xu, K. Krishnamurthy, Bruce M. McMillin and Wen Feng Lu. "A Recursive Least Squares Training Algorithm for Multilayer Recurrent Neural Networks" Proceedings of the American Control Conference (1994, Baltimore, MD) Vol. 2 (1994) p. 1712 - 1716 ISSN: 0743-1619 Available at: http://works.bepress.com/k-krishnamurthy/22/