Current Mode Analog Kohonen Neural Network

M.S.T. Talaska, R. Dlugosz, R. Wojtyna
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引用次数: 8

Abstract

In this paper we present Matlab analysis as well as CMOS implementation of an analog current mode Kohonen neural network (KNN). The presented KNN has been realized using several building blocks proposed earlier by the authors, such as: binary tree winner take all circuit, Euclidean distance calculation circuit, adaptive weights change mechanism. The example network contains four neurons, each of them having three weights. There are three input signals applied, which can be currents or voltages converted just at the beginning into currents. The network operates with the clock frequency of 20 MHz, dissipating 1.5 mW of power from 1.5 V supply voltage. For lower operation frequencies power dissipation can be reduced.
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当前模式模拟Kohonen神经网络
本文给出了模拟电流模式Kohonen神经网络(KNN)的Matlab分析和CMOS实现。本文所提出的KNN是利用作者先前提出的几个模块来实现的,如:二叉树赢家通吃电路、欧氏距离计算电路、自适应权值变化机制。示例网络包含四个神经元,每个神经元有三个权重。有三个输入信号,可以是电流,也可以是刚开始转换成电流的电压。该网络以20 MHz的时钟频率运行,从1.5 V的电源电压消耗1.5 mW的功率。对于较低的工作频率,功耗可以降低。
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