Abstract
This paper addresses the multistability problem of n-dimensional memristive neural networks with a class of general nonmonotonic activation functions. Sufficient conditions are proposed for checking the existence of \((2l+3)^n\) equilibria, of which \((l+2)^n\) equilibria are locally exponentially stable. The obtained stability results improve and extend the existing ones. One numerical example is given to illustrate the effectiveness of the obtained results.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant Nos. 61503338 and 61502422) and the Natural Science Foundation of Zhejiang Province, China (Grant Nos. LQ15F030005, LQ15F020006 and LQ15F020008).
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Huang, Y., Chen, S., Xiao, J., Hao, P. (2017). Coexistence and Local Exponential Stability of Multiple Equilibria in Memristive Neural Networks with a Class of General Nonmonotonic Activation Functions. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10261. Springer, Cham. https://doi.org/10.1007/978-3-319-59072-1_42
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DOI: https://doi.org/10.1007/978-3-319-59072-1_42
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