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.
Recommended Citation
Q. Xu et al., "A Recursive Least Squares Training Algorithm for Multilayer Recurrent Neural Networks," Proceedings of the American Control Conference (1994, Baltimore, MD), vol. 2, pp. 1712 - 1716, Institute of Electrical and Electronics Engineers (IEEE), Jun 1994.
The definitive version is available at https://doi.org/10.1109/ACC.1994.752364
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
International Standard Serial Number (ISSN)
0743-1619
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
01 Jun 1994
