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Inverse optimality in the class of Hopfield neural networks with input nonlinearity

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Abstract

This paper presents the chaos suppression problem in the class of Hopfield neural networks (HNNs) with input nonlinearity using inverse optimality approach. Using the inverse optimality technique and based on Lyapunov stability theory, a stabilizing control law, which is optimal with respect to meaningful cost functional, is determined to achieve global asymptotically stability in the closed-loop system. Numerical simulation is performed on a four-dimensional hyper-chaotic HNN to demonstrate the effectiveness of the proposed method.

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Authors and Affiliations

  1. Advanced Power and Control Systems Lab, EE Department, Imam Khomeini International University, Norouzian Street, Qazvin, Iran

    Yousef Farid & Nooshin Bigdeli

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  1. Yousef Farid
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  2. Nooshin Bigdeli
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Correspondence to Yousef Farid.

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Farid, Y., Bigdeli, N. Inverse optimality in the class of Hopfield neural networks with input nonlinearity. Neural Comput & Applic 22, 711–717 (2013). https://doi.org/10.1007/s00521-011-0756-6

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  • DOI: https://doi.org/10.1007/s00521-011-0756-6

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