Associative memory with a controlled chaotic neural network
Neurocomputing
Optimal matching by the transiently chaotic neural network
Applied Soft Computing
Chaotic synchronization of hindmarsh-rose neural networks using special feedback function
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Information Sciences: an International Journal
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Chaos offers several advantages to the Engineer over other non-chaotic dynamics. One is that chaotic systems are often significantly easier to control than other linear or non-linear systems, requiring only small, appropriately timed perturbations to constrain them within specific unstable periodic orbits (UPOs). Another is that chaotic attractors contain an infinite number of these UPOs. If individual UPOs can be made to represent specific internal states of a system, then a chaotic attractor can be turned into an infinite state machine. In this paper we investigate this possibility with respect to chaotic neural networks. We present a method by which a network can self-select UPOs in response to specific input values. These UPOs correspond to network recognition states for these input values.