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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Multistability of Fractional-Order Neural Networks With Unbounded Time-Varying Delays
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Multistability of Fractional-Order Neural Networks With Unbounded Time-Varying Delays

机译:分数阶神经网络的多态性,具有无限的时变延迟

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This article addresses the multistability and attraction of fractional-order neural networks (FONNs) with unbounded time-varying delays. Several sufficient conditions are given to ensure the coexistence of equilibrium points (EPs) of FONNs with concave–convex activation functions. Moreover, by exploiting the analytical method and the property of the Mittag–Leffler function, it is shown that the multiple Mittag–Leffler stability of delayed FONNs is derived and the obtained criteria do not depend on differentiable time-varying delays. In particular, the criterion of the Mittag–Leffler stability can be simplified to M-matrix. In addition, the estimation of attraction basin of delayed FONNs is studied, which implies that the extension of attraction basin is independent of the magnitude of delays. Finally, three numerical examples are given to show the validity of the theoretical results.
机译:本文满足了分数级神经网络(FONNS)的多功能性和吸引力,具有无限的时变延迟。给出了几种充分的条件,以确保FONN的平衡点(EPS)与凹凸激活功能共存。此外,通过利用分析方法和Mittag-Leffler函数的性质,结果显示推导出延迟FONN的多个Mittag-Lyffer稳定性,并且获得的标准不依赖于可微分的时变延迟。特别地,可以简化Mittag-Leffler稳定性的标准到M矩阵。此外,研究了延迟FONN的吸引力盆地的估计,这意味着吸引盆地的延伸与延迟的幅度无关。最后,给出了三个数值例子来显示理论结果的有效性。

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