IMPULSIVE EFFECT OF CONTINUOUS-TIME NEURAL NETWORKS UNDER PURE STRUCTURAL VARIATIONS.

By using the continuation theorem of coincidence degree theory and constructing suitable Lyapunov functions, we study the existence, uniqueness and global exponential stability of periodic solution for continuous-time neural networks under pure structural variations with impulsive perturbations: \[ \left\{\begin{array}{l} \displaystyle\frac{du_{j}(t)}{dt} = -b_{j}u_{j} (t) + \displaystyle\sum_{k=1}^{n}A_{jk}s_{jk}(t)g_{k}(u_{k}(t)) + U_{j} (t), \quad t>0, \enskip t\neq t_i,\\[15pt] \Delta u_{j}(t_i)=u_{j}(t_{i}^{+})-u_{j}(t_{i}^{-})=I_{j}(u_{j}(t_i)),\quad i=1,2,\ldots; \enskip d j=1,2,\ldots,n. \end{array}\right. \] The results extend earlier ones where impulses are absent. Further, using numerical simulation method the influences of the impulsive perturbations on the inherent oscillation are investigated. [ABSTRACT FROM AUTHOR]