Abstract
We present new global convergence results of neural networks with delays and show that these results partially generalize recently published convergence results by using the theory of monotone dynamical systems. We also show that under certain conditions, reversing the directions of the coupling between neurons preserves the global convergence of neural networks.
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Zhao, W., Zhang, H. (2006). Global Convergence of Continuous-Time Recurrent Neural Networks with Delays. 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_16
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DOI: https://doi.org/10.1007/11759966_16
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