A Unifying Proof of Global Asymptotical Stability of Neural Networks with Delay

Copyright © (2005) by IEEE. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distrubuted for profit. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee.

We present some new global stability results of neural networks with delay and show that these results generalize recently published stability results. In particular, several different stability conditions in the literature whichwere proved using different Lyapunov functionals are generalized and unified by proving them using the same Lyapunov functional. We also show that under certain conditions, reversing the directions of the coupling between neurons preserves the global asymptotical stability of the neural network.

By: Ying Sue Huang, Chai Wah Wu

Published in: IEEE Transactions on Circuits and Systems. Part II: Analog and Digital Signal Processing, volume 52, (no 4), pages 181-4 in 2005


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