Sinusoidal modulation control method in a chaotic neural network |
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| Institution: | 1. School of Business Administration, Shanghai Finance University, Shanghai 201209, China;2. College of Information Technology, Shanghai Ocean University, Shanghai 201306, China;3. School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, China;1. School of Mathematics and Computer Science, Fujian Normal University, 350007 Fuzhou, PR China;2. Department of Mathematics, Shanghai Normal University, 200234 Shanghai, PR China;1. Laboratoire Interdisciplinaire des Sciences et Sciences Appliquées du Sahel (LISSAS), Département des Sciences Physiques, Université de Maroua, BP 46 Maroua, Cameroon;2. Department of Mathematics, Zhejiang Normal University, Jinhua, Zhejiang 321004, PR China;3. Department of Mathematics, Kunming University of Science and Technology, Kunming, Yunnan 650093, PR China;4. Laboratoire LE2I UMR CNRS 6306, Aile des Sciences de l’ingénieur, Université de Bourgogne, BP 47870, 21078 Dijon Cedex, France;1. School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, People’s Republic of China;2. Jiangxi University of Science and Technology, Ganzhou 341000, People’s Republic of China;3. Wuhan Center for Magnetic Resonance, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, People’s Republic of China |
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| Abstract: | Chaotic neural networks (CNNs) have chaotic dynamic associative memory properties: The memory states appear non-periodically, and cannot be converged to a stored pattern. Thus, it is necessary to control chaos in a CNN in order to recognize associative memory. In this paper, a novel control method, the sinusoidal modulation control method, has been proposed to control chaos in a CNN. In this method, a sinusoidal wave simplified from brain waves is used as a control signal to modulate a parameter of the CNN. The simulation results demonstrate the effectiveness of this control method. The controlled CNN can be applied to information processing. Moreover, the method provides a way to associate brain waves by controlling CNNs. |
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| Keywords: | Controlling chaos Chaotic neural network Brain wave Information processing |
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