Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1863232 | Physics Letters A | 2007 | 6 Pages |
Abstract
The chaos control in the chaotic neural network is studied using the partial state feedback with a control signal from a few control neurons. The controlled CNN converges to one of the stored patterns with a period which depends on the initial conditions, i.e., the set of control neurons and other control parameters. We show that the controlled CNN can distinguish between two initial patterns even if they have a small difference. This implies that such a controlled CNN can be feasibly applied to information processing such as pattern recognition.
Related Topics
Physical Sciences and Engineering
Physics and Astronomy
Physics and Astronomy (General)
Authors
Guoguang He, Manish Dev Shrimali, Kazuyuki Aihara,