CAJ | 학술논문

突破以往混沌神经元模型以Sigmoid函数作为激励函数的过程，构建了由高斯函数独自作为激励函数的混沌神经元模型，分析了它的混沌动力学特性；撤销模拟退火策略后，通过对时间序列的重要指标，如功率谱及最大Lyapunov指数的分析，证实高斯激励的神经元动力系统能够保持永久的混沌搜索状态；利用该系统对灰度图像进行加密，阐述了其原理及算法，通过对加密前后直方图的考查，说明了该混沌加密算法具有较强的抗统计分析能力。
돌파이왕혼돈신경원모형이Sigmoid함수작위격려함수적과정，구건료유고사함수독자작위격려함수적혼돈신경원모형，분석료타적혼돈동역학특성；철소모의퇴화책략후，통과대시간서렬적중요지표，여공솔보급최대Lyapunov지수적분석，증실고사격려적신경원동력계통능구보지영구적혼돈수색상태；이용해계통대회도도상진행가밀，천술료기원리급산법，통과대가밀전후직방도적고사，설명료해혼돈가밀산법구유교강적항통계분석능력。
The original chaotic neural model with Sigmoid as activation function has been bro-ken through .The novel chaotic neural model with Gauss activation function was constructed . The characteristic of the chaotic dynamics was analyzed .After removing the simulated annea-ling strategy , the chaotic searching state can be forever kept .The important targets of the time series such as the Lyapunov exponent and the power spectrum have been analyzed .This system was applied to encrypt for the gray image .The principle and the algorithm were illu-minated for this application .The capability of resisting statistic was proved through checking the histogram of the original image and the encrypted image .