Novel global exponential stability criteria for hybrid BAM neural networks with proportional delays ?
Proportional delay is a kind of unbounded time-varying delay, which is different from unbounded distributed delays. A class of hybrid BAM neural networks with proportional delays is concerned in this paper. First, by choosing suitable nonlinear variable transformations, the hybrid BAM neural networks with proportional delays can be equivalently transformed into the hybrid BAM neural networks with constant delays and variable coefficients. Then, in virtue of Brouwer?s fixed point theorem, the existence and uniqueness of equilibrium point of the system are proved. Furthermore, by constructing appropriate delay differential inequalities, several new delay-independent and delay-dependent global exponential stability sufficient conditions of equilibrium point of the system are obtained, and can be easily checked by simple algebraic calculation. Finally, several numerical examples are given to illustrate the effectiveness the obtained results and less conservative than some existing results.
global exponential stability hybrid bam neural networks proportional delays delay differential inequality