Global Asymptotic Stability Conditions of Delayed Neural Networks
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摘要: 利用Liapunov泛函方法,结合矩阵不等式技巧,分析了时滞细胞神经网络(DCNNs)的平衡点存在的唯一性和全局渐近稳定性,保证DCNNs的全局稳定性的一个新的充分判据被得到.所得判据提供了一些参数来适当地弥补了反馈矩阵与时滞反馈矩阵之间所需要的平衡关系.这些判据可以容易被使用来设计和检验全局稳定的网络.此外,所得判据是与时滞参数无关,且比已有文献具有更少的限制.Abstract: Utilizing the Liapunov functional method and combining the inequality of matrices technique to analyze the existence of a unique equilibrium point and the global asymptotic stability for delayed cellular neural networks (DCNNs), a new sufficient criterion ensuring the global stability of DCNNs is obtained. Our criteria provide some parameters to appropriately compensate for the tradeoff between the matrix definite condition on feedback matrix and delayed feedback matrix. The criteria can easily be used to design and verify globally stable networks. Furthermore, the condition presented here is independent of the delay parameter and is less restrictive than that given in the references.
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Key words:
- cellular neural network /
- global stability /
- inequality of matrix /
- delay
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