Global asymptotical ω -periodicity of a fractional-order non-autonomous neural networks

摘要: We study the global asymptotic ω -periodicity for a fractional-order non-autonomous neural networks. Firstly, based on Caputo derivative it is shown that -periodic or autonomous networks cannot generate exactly signals. Next, by using contraction mapping principle we discuss existence and uniqueness of S-asymptotically solution class Then differential integral inequality technique, Mittag-Leffler stability asymptotical periodicity networks, which shows all paths starting from arbitrary points responding to persistent, nonconstant external inputs, asymptotically converge same function may be not solution. The non-existence solutions periodic (FNN).Proof result FNN.A new concept fractional equations.Some sufficient conditions FNN.