Abstract
This paper considers the problem of stability analysis for discrete-time recurrent neural networks with randomly occurred nonlinearities (RONs) and time-varying delay. By utilizing new Lyapunov-Krasovskii functions and delay-partitioning technique, the stability criteria are proposed in terms of linear matrix inequality (LMI). We have also shown that the conservatism of the conditions is a non-increasing function of the number of delay partitions. A numerical example is provided to demonstrate the effectiveness of the proposed approach.
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Duan, J., Hu, M., Yang, Y. (2013). A Delay-Partitioning Approach to Stability Analysis of Discrete-Time Recurrent Neural Networks with Randomly Occurred Nonlinearities. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7951. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39065-4_25
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DOI: https://doi.org/10.1007/978-3-642-39065-4_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39064-7
Online ISBN: 978-3-642-39065-4
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