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Proceedings Paper

Quantitative analysis of exponential stability of nonlinear continuous neural networks
Author(s): Lisheng Wang; Zheng Tan; Rongsheng Huang
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Paper Abstract

In this paper, a characteristic function is defined and used to quantitatively characterize exponential stability of nonlinear continuous neural network. By utilizing the function, we address many important aspects of network, including global and local exponential stability; the estimates of the domain of attraction of stable equilibrium point; the estimates of convergent rate of the network trajectories. A sufficient and necessary condition for network to be locally exponentially stable is obtained. Our method is simple and practical, and our results generalize those in 1-3.

Paper Details

Date Published: 25 September 1998
PDF: 4 pages
Proc. SPIE 3545, International Symposium on Multispectral Image Processing (ISMIP'98), (25 September 1998); doi: 10.1117/12.323602
Show Author Affiliations
Lisheng Wang, Xi'an Jiaotong Univ. and Huazhong Univ. of Science and Technology (China)
Zheng Tan, Xi'an Jiaotong Univ. and Huazhong Univ. of Science and Technology (China)
Rongsheng Huang, Xi'an Jiaotong Univ. and Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 3545:
International Symposium on Multispectral Image Processing (ISMIP'98)
Ji Zhou; Anil K. Jain; Tianxu Zhang; Yaoting Zhu; Mingyue Ding; Jianguo Liu, Editor(s)

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