1Department of Mathematics, BJB Autonomous College, Bhubaneswar, Orissa, INDIA. E-mail:
2Icfai Business School, Plot No-123, Mancheswar Industrial Estate, Bhubaneswar, Orissa, INDIA. E-mail:
AMS subject classification:
Convergence and stability are important features of neural network. It has been observed that the neural network leads to instability with time-vary delays. In this present paper we have made an attempt to establish the stability of neural network by considering logarithmic approach, where the activation function is assumed to be globally lipschitz continous. The sufficient condition ensuring the delayed neural network for attaining unique equilibrium point which is globally stable, has been proved by using linear matrix inequality approach (LMI).
Neural networks, global logarithmic stability, Lipschitz continous, time-varying delay system, linear matrix inequality, activation function
