Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/72844
Title: Parameter estimation using artificial neural nets
Authors: Loh, A.P. 
Lee, T.H. 
Issue Date: 1991
Source: Loh, A.P.,Lee, T.H. (1991). Parameter estimation using artificial neural nets. IFAC Symposia Series (7) : 81-83. ScholarBank@NUS Repository.
Abstract: Artificial neural nets have become increasingly popular in solving optimization problems such as the Travelling Salesman Problem, A/D converter realization, linear programming and pattern recognition. In many of these optimization problems, the neural net structure is based on the Hopfield model and each neuron is assumed to have two distinct outputs, 0 or 1. In this paper, a continuous Hopfield net is used to formulate and solve a least squares minimization problem associated with the parameter estimation of a physical system. The algorithm shows good convergence and a certain amount of `recursion' can be incorporated which makes it useful for real time applications.
Source Title: IFAC Symposia Series
URI: http://scholarbank.nus.edu.sg/handle/10635/72844
ISBN: 0080409350
ISSN: 09629505
Appears in Collections:Staff Publications

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