Geodesic
Robustness analysis of global exponential stability in neural networks evoked by deviating argument and stochastic disturbance :
Wan, Liguang 1   ; Wu, Ailong 2   ; Chen, Jingru 3
1 College of Mechatronics and Control Engineering, Hubei Normal University, Huangshi 435002, China
2 College of Mathematics and Statistics, Hubei Normal University, Huangshi 435002, China
3 Department of Personnel, Hubei Normal University, Huangshi 435002, China
Journal of nonlinear sciences and its applications, Tome 10 (2017) no. 11, p. 5646-5667 Cet article a éte moissonné depuis la source International Scientific Research Publications

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This paper studies the robustness of global exponential stability of neural networks evoked by deviating argument and stochastic disturbance. Given the original neural network is globally exponentially stable, we discuss the problem that the neural network is still globally exponentially stable when the deviating argument or both the deviating argument and stochastic disturbance is/are generated. By virtue of solving the derived transcendental equation(s), the upper bound(s) about the intensity of the deviating argument or both of the deviating argument and stochastic disturbance is/are received. The obtained theoretical results are the supplements to the existing literatures on global exponential stability of neural networks. Two numerical examples are offered to demonstrate the effectiveness of theoretical results.

DOI : 10.22436/jnsa.010.11.04
Classification : 34D23, 93D09
Keywords: Global exponential stability, robustness, neural networks, deviating argument, stochastic disturbance

Wan, Liguang   1   ; Wu, Ailong   2   ; Chen, Jingru   3

1 College of Mechatronics and Control Engineering, Hubei Normal University, Huangshi 435002, China
2 College of Mathematics and Statistics, Hubei Normal University, Huangshi 435002, China
3 Department of Personnel, Hubei Normal University, Huangshi 435002, China 
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Wan, Liguang ; Wu, Ailong ; Chen, Jingru  . Robustness analysis of global exponential stability in neural networks evoked by deviating argument and stochastic disturbance. Journal of nonlinear sciences and its applications, Tome 10 (2017) no. 11, p. 5646-5667. doi: 10.22436/jnsa.010.11.04

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