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DISCRETE-TIME HOPFIELD NEURAL NETWORK: CONVERGENCE THEOREM: PERTURBATION ANALYSIS
  • Rama Murthy Garimella
Rama Murthy Garimella
Mahindra University, Mahindra University

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Abstract

In this research paper, we show that the convergence theorem of Hopfield neural network hods true without the condition that the diagonal elements must be non-negative (using epsilon perturbation of diagonal elements). More general version of this result is also proved. It is also shown that the eigenvalue vector of a symmetric matrix and the diagonal element vector are related through a linear system of equations with the coefficient matrix being a doubly stochastic matrix. Also, Perturbation analysis of Hopfield neural network is discussed.