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Dynamic analysis of a general class of winner-take-all competitive neural networks
- Source :
- IEEE transactions on neural networks. 21(5)
- Publication Year :
- 2010
-
Abstract
- This paper studies a general class of dynamical neural networks with lateral inhibition, exhibiting winner-take-all (WTA) behavior. These networks are motivated by a metal-oxide-semiconductor field effect transistor (MOSFET) implementation of neural networks, in which mutual competition plays a very important role. We show that for a fairly general class of competitive neural networks, WTA behavior exists. Sufficient conditions for the network to have a WTA equilibrium are obtained, and rigorous convergence analysis is carried out. The conditions for the network to have the WTA behavior obtained in this paper provide design guidelines for the network implementation and fabrication. We also demonstrate that whenever the network gets into the WTA region, it will stay in that region and settle down exponentially fast to the WTA point. This provides a speeding procedure for the decision making: as soon as it gets into the region, the winner can be declared. Finally, we show that this WTA neural network has a self-resetting property, and a resetting principle is proposed.
- Subjects :
- Neurons
Class (computer programming)
Artificial neural network
Computer Networks and Communications
Computer science
business.industry
Property (programming)
Neural Inhibition
General Medicine
Winner-take-all
Computer Science Applications
Game Theory
Nonlinear Dynamics
Artificial Intelligence
Lateral inhibition
Biomimetics
Humans
Computer Simulation
Artificial intelligence
Neural Networks, Computer
business
Software
Algorithms
Subjects
Details
- ISSN :
- 19410093
- Volume :
- 21
- Issue :
- 5
- Database :
- OpenAIRE
- Journal :
- IEEE transactions on neural networks
- Accession number :
- edsair.doi.dedup.....f0500875a506740ecab7283898383b23