Abstract
In this paper, a sufficient condition is presented to ensure the complete stability of a cellular neural networks (CNNs) that output functions are a set of piecewise sigmoid nonlinear functions. The convergence theorem of the Gauss-Seidel method and Gauss-Seidel method, which is an iterative technique for solving a linear algebraic equation, plays an important role in our discussion.
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© 2006 Springer-Verlag Berlin Heidelberg
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Zhou, Lq., Hu, Gd. (2006). A New Sufficient Condition on the Complete Stability of a Class Cellular Neural Networks. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_32
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DOI: https://doi.org/10.1007/11759966_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34439-1
Online ISBN: 978-3-540-34440-7
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