手描逻辑符线路图的神经网自学习系统

A NEURAL NETWORK SYSTEM FOR THE SELF-LEARNING RECOGNITION OF HAND-DRAWING LOGIC DIAGRAM

本文描述了一个在多层神经网中利用离散化正向传输,以输出均方误差的随机变量为指导,进行总体方向上权网的肯定——否定非线性学习方法。离散化正向传输减少了神经元上保持值的种类数目。总体方向上的肯定——否定简化了自学习算法。该方法摆脱了较复杂的反向传输计算而不失其学习速度。我们把这种方法称作DSGNN算法。DSGNN更适合于并行处理和生物视觉模拟。DSGNN已成功地应用于手描逻辑符线路图自学习识别系统,识别准确率在95%以上。

We describe a method for solving nonlinear supervised learning tasks bymaking forward broadcast discretized and globally training weight arrays in themultilayer neural network by the affirmation-negation way with guide of the stocha-stic variable of output mean squared error.The discretized forward broadcast decrea-ses in number of kinds of the hold-value on neurons.The algorithm of self-learningis gobally simplified by the affirmation-negation.The method dispenses with the morecomplicated back-propagation computation,and yet it dosen't lose its learning speed.This method is called as DSGNN algorithm.DSGNN may be more suitable to parallelimplementation and biological perspective simulation.DSGNN has been successfullyapplied to the system for self-learning recognition of hand-drawing logic diagram.The rate of recognition accuracy is above 95%.