一类变系数混合时滞神经网络周期解的存在性和指数稳定性
일류변계수혼합시체신경망락주기해적존재성화지수은정성
On Exponential Periodicity and Stability of a New Class of Neural Networks with Variable Coefficients and Mixed Delays
  • 万方数据
    应用数学 응용수학
    MATHEMATICA APPLICATA
    2009年 4期 876-882 ,共7页

    田安峰%盖明久%黄诘

    전안봉%개명구%황힐

    周期解%指数稳定%神经网络%混合时滞 週期解%指數穩定%神經網絡%混閤時滯
    주기해%지수은정%신경망락%혼합시체
    Periodic solution%Exponential stability%Neural networks%Mixed delays
    在不需要激活函数有界的条件下,利用分析技巧和Poincaré映射给出了一类变系数混合时滞神经网络周期解的存在、唯一和指数稳定的充分条件.结论对神经网络的设计具有重要的意义.
    재불수요격활함수유계적조건하,이용분석기교화Poincaré영사급출료일류변계수혼합시체신경망락주기해적존재、유일화지수은정적충분조건.결론대신경망락적설계구유중요적의의.
    By applying Poincaré mapping and analysis technique,sufficient conditions were established to guarantee existence,uniqueness and globally exponentially stable of periodic solution of a new class of neural networks with variable coefficients and mixed delays.Without assuming the boundedness of active functions,the results have much significance in design neural networks.
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