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)


Mechanical and Aerospace Engineering

Second Department

Computer Science



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)


Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 1994 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jun 1994