Abstract
The problem of global robust stability of neural networks with time delays and uncertainties is investigated. The uncertainties are assumed to be norm-bounded. The problem is discussed based on the Lyapunov method and linear matrix inequality (LMI) techniques. A novel criterion is given to ascertain the robust stability of the system. The criterion is expressed in terms of LMIs. It is computationally efficient, since the LMIs can be easily solvable by various convex optimization algorithms.
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