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首頁> 外文期刊>IEEE Transactions on Circuits and Systems. I, Regular Papers >An approach to information propagation in 1-D cellular neuralnetworks-Part I: Local diffusion
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An approach to information propagation in 1-D cellular neuralnetworks-Part I: Local diffusion

機(jī)譯：一維細(xì)胞神經(jīng)網(wǎng)絡(luò)中信息傳播的方法-第一部分：局部擴(kuò)散

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This is the first of two companion papers devoted to a deep analysis of the dynamics of information propagation in the simplest nontrivial Cellular Neural Network (CNN), which is one-dimensional and has connections between nearest neighbors only. We will show that two behaviors are possible: local diffusion of information between neighboring cells and global propagation through the entire array. This paper deals with local diffusion, of which we will first give an accurate definition, before computing the template parameters for which the CNN has this behavior. Next we will compute the number of stable equilibria, before examining the convergence of any trajectory toward them, for three different kinds of boundary conditions: fixed Dirichlet, reflective, and periodic
機(jī)譯：這是致力于深度分析最簡單的非平凡細(xì)胞神經(jīng)網(wǎng)絡(luò)（CNN）中信息傳播動(dòng)態(tài)的兩篇相伴論文中的第一篇，該論文是一維的，并且僅在最近的鄰居之間具有連接。我們將證明兩種行為是可能的：相鄰單元之間信息的局部擴(kuò)散和整個(gè)陣列的全局傳播。本文涉及局部擴(kuò)散，在計(jì)算CNN具有此行為的模板參數(shù)之前，我們將首先對(duì)其進(jìn)行精確定義。接下來，我們將針對(duì)三種不同的邊界條件，在檢查任何軌跡朝其收斂之前，將計(jì)算穩(wěn)定均衡的數(shù)量：固定狄利克雷，反射和周期性

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