2001
DOI: 10.1016/s0960-0779(00)00200-9
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Chaos control of Bonhoeffer–van der Pol oscillator using neural networks

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Cited by 49 publications
(19 citation statements)
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“…Different classes of neutral networks such as Hopfield neural networks, cellular neural networks, Lotka-Volterra neural networks, Cohen-Grossberg neural networks, and bidirectional associative memory neural networks have been extensively studied [1][2][3][4]. Recently, it has been well recognized that time delays are often encountered in various neural networks, and the delays are often the sources of oscillations, instability and poor performance of the networks [5][6][7][8][9][10][11][12].…”
Section: Introductionmentioning
confidence: 99%
“…Different classes of neutral networks such as Hopfield neural networks, cellular neural networks, Lotka-Volterra neural networks, Cohen-Grossberg neural networks, and bidirectional associative memory neural networks have been extensively studied [1][2][3][4]. Recently, it has been well recognized that time delays are often encountered in various neural networks, and the delays are often the sources of oscillations, instability and poor performance of the networks [5][6][7][8][9][10][11][12].…”
Section: Introductionmentioning
confidence: 99%
“…An extended BVP oscillator can show a stable oscillator as well as a stable threshold phenomenon [23]. The extended BVP oscillator has been observed numerically and experimentally in certain systems and has attributes of great utility to medicine, chemical kinetics, neuroscience, electrical circuits and secure communications [15], [23][24][25]. The extended BVP model consists of an inductor, two capacitors and a linear resistor.…”
Section: The Extended Bonhoeffer Van-der Pol Chaotic Oscillatormentioning
confidence: 99%
“…Note that by using some numerical techniques, the fixed points of a chaotic system whose states are accessible can be calculated without using its dynamic equation (Ramesh andNarayanan 2001, Schmelcher andDiakonos, 1997) hence it is assumed that the fixed points of the system are obtained by a numerical algorithm without using the system parameters.…”
Section: Problem Statementmentioning
confidence: 99%