一类神经网络的全局稳定性分析
Global Stability Analysis of Neural Networks
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摘要: 研究一类Hopfield型神经网络的平衡点的存在性、唯一性和全局稳定性。在放弃神经网络激活函数的 有界性、单调递增性和可微性条件下,得到了神经网络平衡点的存在性和唯一性条件;利用M矩阵理论,通过构 造适当的Liapunov函数,得到神经网络全局稳定性条件。这些条件适用于神经网络中关联矩阵为对称或非对 称、激活函数为非单调的情况。Abstract: An analysis is made of the existence, uniqueness and globally asymptotical stability of the equilibrium point of Hopfield neural networks. Without assuming the boundedness, monotonicity and differentiability of the activation functions, the conditions ensuring existence and uniqueness of the equilibrium are obtained. Using M-matrix theory, Liapunov function is constructed and employed to establish sufficient conditions for global asymptotic stability. These results are applicable to symmetric or nonsymmetric interconnection matrices, and to continuous non-monotonic neuron activation function
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Key words:
- neural networks /
- stability /
- M-matrix
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