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 difficult 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 identification, 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 clarifies the application prospect of the associative dynamics of the chaotic neural network.

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Elimination of spiral waves in a locally connected chaotic neural network by a dynamic phase space constraint

Neural Networks ◽

10.1016/j.neunet.2017.01.002 ◽

2017 ◽

Vol 88 ◽

pp. 9-21 ◽

Cited By ~ 2

Author(s):

Yang Li ◽

Makito Oku ◽

Guoguang He ◽

Kazuyuki Aihara

Keyword(s):

Neural Network ◽

Phase Space ◽

Space Constraint ◽

Spiral Waves ◽

Chaotic Neural Network ◽

Dynamic Phase

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State Feedback Method to Control Chaotic Neural Network Based on the Dynamic Phase-Space Constraint

Iranian Journal of Science and Technology Transactions of Electrical Engineering ◽

10.1007/s40998-021-00407-y ◽

2021 ◽

Author(s):

Nahid Abolpour ◽

Reza Boostani ◽

Mohammad Ali Masnadi-Shirazi

Keyword(s):

Neural Network ◽

Phase Space ◽

Space Constraint ◽

State Feedback ◽

Chaotic Neural Network ◽

Dynamic Phase ◽

Feedback Method

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A Novel Chaotic Neural Network Using Memristive Synapse with Applications in Associative Memory

Abstract and Applied Analysis ◽

10.1155/2012/405739 ◽

2012 ◽

Vol 2012 ◽

pp. 1-19 ◽

Cited By ~ 10

Author(s):

Xiaofang Hu ◽

Shukai Duan ◽

Lidan Wang

Keyword(s):

Neural Network ◽

Associative Memory ◽

Large Scale ◽

Cmos Technology ◽

Chaotic Neural Network ◽

Circuit Element ◽

Nanoscale Cmos ◽

Dynamical Behaviors ◽

Implementation Scheme ◽

Further Development

Chaotic Neural Network, also denoted by the acronym CNN, has rich dynamical behaviors that can be harnessed in promising engineering applications. However, due to its complex synapse learning rules and network structure, it is difficult to update its synaptic weights quickly and implement its large scale physical circuit. This paper addresses an implementation scheme of a novel CNN with memristive neural synapses that may provide a feasible solution for further development of CNN. Memristor, widely known as the fourth fundamental circuit element, was theoretically predicted by Chua in 1971 and has been developed in 2008 by the researchers in Hewlett-Packard Laboratory. Memristor based hybrid nanoscale CMOS technology is expected to revolutionize the digital and neuromorphic computation. The proposed memristive CNN has four significant features: (1) nanoscale memristors can simplify the synaptic circuit greatly and enable the synaptic weights update easily; (2) it can separate stored patterns from superimposed input; (3) it can deal with one-to-many associative memory; (4) it can deal with many-to-many associative memory. Simulation results are provided to illustrate the effectiveness of the proposed scheme.

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