A Type of Delay Feedback Control of Chaotic Dynamics in a Chaotic Neural Network

A chaotic neural network consisting of chaotic neurons exhibits such rich dynamical behaviors as nonperiodic associative memory. But it is difficult to distinguish the stored patterns from others, since the chaotic neural network shows chaotic wandering around the stored patterns. In order to apply the nonperiodic associative memory to information search or pattern identification, it is necessary to control chaotic dynamics. In this paper, we propose a delay feedback control method for the chaotic neural network. Computer simulation shows that, by means of the control method, the chaotic dynamics in the chaotic neural network are changed. The output sequence of the controlled network wanders around one stored pattern and its reverse pattern

Parrondo's games with chaotic switching

This paper investigates the different effects of chaotic switching on Parrondo's games, as compared to random and periodic switching. The rate of winning of Parrondo's games with chaotic switching depends on coefficient(s) defining the chaotic generator, initial conditions of the chaotic sequence and the proportion of Game A played. Maximum rate of winning can be obtained with all the above mentioned factors properly set, and this occurs when chaotic switching approaches periodic behavior.Comment: 11 pages, 9 figure

Synchronization of Chaotic Oscillators due to Common Delay Time Modulation

We have found a synchronization behavior between two identical chaotic systems^M when their delay times are modulated by a common irregular signal. ^M This phenomenon is demonstrated both in two identical chaotic maps whose delay times are driven by a common^M chaotic or random signal and in two identical chaotic oscillators whose delay times are driven by^M a signal of another chaotic oscillator. We analyze the phenomenon by using^M the Lyapunov exponents and discuss it in relation with generalized synchronization.^MComment: 5 pages, 4 figures (to be published in PRE

Controlling Chaos in a Neural Network Based on the Phase Space Constraint

The chaotic neural network constructed with chaotic neurons exhibits very rich dynamic behaviors and has a nonperiodic associative memory. In the chaotic neural network, however, it is dicult to distinguish the stored patters from others, because the states of output of the network are in chaos. In order to apply the nonperiodic associative memory into information search and pattern identication, etc, it is necessary to control chaos in this chaotic neural network. In this paper, the phase space constraint method focused on the chaotic neural network is proposed. By analyzing the orbital of the network in phase space, we chose a part of states to be disturbed. In this way, the evolutional spaces of the strange attractors are constrained. The computer simulation proves that the chaos in the chaotic neural network can be controlled with above method and the network can converge in one of its stored patterns or their reverses which has the smallest Hamming distance with the initial state of the network. The work claries the application prospect of the associative dynamics of the chaotic neural network