High-order absolutely stable neural networks
- 1 January 1991
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Circuits and Systems
- Vol. 38 (1), 57-65
- https://doi.org/10.1109/31.101303
Abstract
The stability properties of arbitrary order continuous-time dynamic neural networks are studied in the spirit of an earlier analysis of a first-order system by Cohen and Grossberg. The corresponding class of Lyapunov functions is presented and the equilibrium points are characterized. The relationships with other continuous-time models are pointed out.This publication has 23 references indexed in Scilit:
- Analysis and synthesis of a class of neural networks: variable structure systems with infinite grainIEEE Transactions on Circuits and Systems, 1989
- Qualitative analysis of neural networksIEEE Transactions on Circuits and Systems, 1989
- Higher order associative memories and their optical implementationsNeural Networks, 1988
- Cellular neural networks: theoryIEEE Transactions on Circuits and Systems, 1988
- Qualitative analysis and synthesis of a class of neural networksIEEE Transactions on Circuits and Systems, 1988
- Neural networks for nonlinear programmingIEEE Transactions on Circuits and Systems, 1988
- A study on neural networksInternational Journal of Intelligent Systems, 1988
- ART 2: self-organization of stable category recognition codes for analog input patternsApplied Optics, 1987
- The capacity of the Hopfield associative memoryIEEE Transactions on Information Theory, 1987
- A massively parallel architecture for a self-organizing neural pattern recognition machineComputer Vision, Graphics, and Image Processing, 1987