Abstract
This paper presents the results of stability analysis of a general class of continuous-time recurrent neural networks. The new stability results includes sufficient conditions for global asymptotic stability. With weaker conditions and less restrictive activation functions, the new stability results improve and extend existing ones. Discussions and examples are given to illustrate and compare the new results with the existing ones.
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Fu, C., Wang, Z. (2009). Stability Analysis of a General Class of Continuous-Time Recurrent Neural Networks. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5551. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01507-6_40
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DOI: https://doi.org/10.1007/978-3-642-01507-6_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01506-9
Online ISBN: 978-3-642-01507-6
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