Neural networks for system identification
- 1 April 1990
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Control Systems Magazine
- Vol. 10 (3), 31-35
- https://doi.org/10.1109/37.55121
Abstract
Two approaches are presented for utilization of neural networks in identification of dynamical systems. In the first approach, a Hopfield network is used to implement a least-squares estimation for time-varying and time-invariant systems. The second approach, which is in the frequency domain, utilizes a set of orthogonal basis functions and Fourier analysis to construct a dynamic system in terms of its Fourier coefficients. Mathematical formulations are presented, along with simulation results.Keywords
This publication has 4 references indexed in Scilit:
- Simple 'neural' optimization networks: An A/D converter, signal decision circuit, and a linear programming circuitIEEE Transactions on Circuits and Systems, 1986
- “Neural” computation of decisions in optimization problemsBiological Cybernetics, 1985
- Neurons with graded response have collective computational properties like those of two-state neurons.Proceedings of the National Academy of Sciences, 1984
- Neural networks and physical systems with emergent collective computational abilities.Proceedings of the National Academy of Sciences, 1982