A New Stability Condition of Discrete Hopfield Neural Networks with Weight Function Matrix

Y. Zhang, Jun Li, Zheng-wang Ye
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引用次数: 3

Abstract

The stability analysis of discrete Hopfield neural networks(DHNNs) not only has an important theoretical significance, but also can be widely used in the associative memory, combinatorial optimization, etc. Most matrixes of DHNNs and DHNNs with delay are constant matrixes. The stability of discrete Hopfield neural networks with weight function matrix (DHNNWFM) is studied. A new stability condition for DHNNWFM is presented by using the Lyapunov method.
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具有权函数矩阵的离散Hopfield神经网络的一个新的稳定性条件
离散Hopfield神经网络(DHNNs)的稳定性分析不仅具有重要的理论意义,而且在联想记忆、组合优化等方面具有广泛的应用前景。dhnn和具有延迟的dhnn的矩阵大多是常数矩阵。研究了具有权函数矩阵的离散Hopfield神经网络的稳定性。利用李雅普诺夫方法给出了DHNNWFM的一个新的稳定条件。
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