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 metaloxidesemiconductor 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. © 2010 IEEE.
| Original language | English |
|---|---|
| Article number | 5427042 |
| Pages (from-to) | 771-783 |
| Journal | IEEE Transactions on Neural Networks |
| Volume | 21 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - May 2010 |
| Externally published | Yes |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Research Keywords
- Competition
- Convergence analysis
- Lateral inhibition
- Neural networks
- Neurodynamics
- Shunting and additive
- Very large scale integration (VLSI) neural networks
- Winner-take-all (WTA)