机动车号牌抓拍联网比对报警系统的研究【研究类】【无图】
收藏
资源目录
压缩包内文档预览：
编号：271453
类型：共享资源
大小：319.70KB
格式：RAR
上传时间：2014-04-05
上传人：上***
认证信息
个人认证
高**（实名认证）
江苏
IP属地：江苏
35
积分
- 关 键 词：
-
机动车
号牌
抓拍
联网
对于
报警
系统
研究
钻研
- 资源描述：
-
机动车号牌抓拍联网比对报警系统的研究
19页 11000字数+说明书+开题报告+任务书+文献综述
中期质量检查安排.doc
任务书.doc
外文翻译--基于人工神经网络的车牌照识别.doc
封面.doc
文献综述.doc
机动车号牌抓拍联网比对报警系统的研究开题报告.doc
机动车号牌抓拍联网比对报警系统的研究论文.doc
目 录
摘 要0
1引言1
1.1课题研究的目的意义1
1.2研究现状1
1.3发展趋势与研究方向2
1.3.1车牌的定位与分割2
1.3.2字符识别2
1.3.3信号传输、数据比对及自动报警3
2识别系统的原理和组成3
2.1识别系统的原理3
2.2触发单元4
2.2.1触发单元硬件设计4
2.2.2单片机红外控制的设计4
2.2.3脉冲信号的设计4
2.2.4单片机红外遥控的实现5
2.3抓拍单元5
2.4处理单元5
2.4.1图像转换及压缩5
2.4.2图像增强5
2.4.3图像水平校正6
2.4.4边缘检测6
2.4.5 Hough变换6
2.4.6车牌定位7
2.4.7字符分割7
2.4.8字符识别8
3自动报警10
3.1自动报警集成系统的硬件组成10
3.1.1GPS 模块与单片机Ⅰ的连接11
3.1.2单片机之间的并口通信11
3.1.3单片机Ⅱ与GSM 收发器的连接12
3.1.4 AT 指令12
3.1.5单片机Ⅰ程序设计12
3.1.6单片机Ⅱ程序设计13
4结束语14
摘 要
汽车牌照自动识别系统是以汽车牌照为特定目标的专用计算机视觉系统。是计算机视觉和模式识别技术在智能交通领域应用的重要研究课题之一。利用车辆智能监测系统对平安大道的建设非常有意义，它可以非常迅速地捕捉到肇事车辆、违章车辆、黑名单车辆等，对公路运行车辆的构成、流量分布、违章情况进行常年不间断的自动记录，为交通规划，交通管理，道路养护部门提供重要的基础和运行数据，为快速纠正交通违章行为，快速侦破交通事故逃逸和机动车盗抢案件提供重要的技术手段和证据，对平安大道的建设、运行和提高公路交通管理的快速反应能力有着十分重要的意义。
机动车号牌抓拍联网比对报警系统是由识别系统、自动报警系统组成。其中识别系统主要包括触发单元、抓拍单元、处理单元。主要介绍触发单元的工作原理及单片机工作电路；抓拍单元主要介绍车辆触发系统受到车辆触发信号后，车辆抓拍单元中摄像机、闪光灯的工作方式和原理；处理单元主要介绍车辆图像的转换及压缩、图像增强、图像水平校正、边缘检测、Hough变换、字符分割、字符识别的软件处理方式；自动报警系统主要介绍基于双单片机的自动报警系统的信息收发，单片机自动报警系统的原理与电路图的设计。
关键词:抓拍系统; 识别系统; 自动报警系统
- 内容简介：
-
中国地质大学长城学院毕业设计（论文）中期检查表学生姓名学号班 级指导教师职称单 位毕业设计（论文）题目已完成工作、存在的问题及下一步的打算 学生签名： 年 月 日检查意见 指导教师签名：年 月 日 中国地质大学长城学院毕业设计（论文）任务书学生姓名郝宝祥学号05208339班 级机制0803班指导教师赵晓顺职称讲师单 位河北农业大学毕业设计（论文）题目机动车号牌抓拍联网比对报警系统的研究毕业设计（论文）主要内容和要求：1、 机动车号牌抓拍联网比对报警系统的设计2、 比对原理及方法的研究3、 论文一份毕业设计（论文）主要参考资料：1 吴李汉,文俊浩. 车牌自动识别系统的设计与实现J. 可编程控制器与工厂自动化, 2006,(09) . 2 张明光. 车辆牌照自动识别系统的研究J. 新课程(教育学术版), 2007,(S3) . 3 王琪. 基于BP神经网络与支持向量机的高速公路交通模式识别J. 科技信息(科学教研), 2007,(34) . 4 井勤. 论指纹识别技术及其应用J. 科技信息(科学教研), 2007,(35) . 5 薛俊韬,王树成,刘正光. 指纹图像的自适应预处理研究J. 计算机工程与设计, 2008,(01) . 6 苏厚胜. 车牌识别系统的设计与实现J. 可编程控制器与工厂自动化, 2006,(03) . 7 李小平,任江兴,杨德刚. 车牌识别系统中若干问题的探讨J. 北京理工大学学报, 2001,(01) . 8 牛欣,沈兰荪. 基于特征的车辆牌照定位算法J. 交通与计算机, 2000,(01) . 9 杜俊俐,张景飞,黄心汉. 基于视觉的象棋棋盘识别J. 计算机工程与应用, 2007,(34) . 10胡延平,何鸿鹏,马德成. 有相对运动的车牌识别技术研究J大连理工大学学报, 2004,(03) .11 张永慧，刘昌平，罗公，李国杰. 技术综合集成在模式识别中的应用J计算机学报, 1995,(09) .12 戚飞虎,叶芗芸,李卫东,孙晓阳. 机动车辆自动识别收费系统J交通与计算机, 1997,(01) .13 牛欣,沈兰荪. 基于特征的车辆牌照定位算法J交通与计算机, 2000,(01) .14 张炜,王庆,赵荣椿. 汽车牌照的实时分割方法J西北工业大学学报, 2001,(01) . 15 魏武,张起森,王明俊,黄中祥. 一种基于模板匹配的车牌识别方法J中国公路学报, 2001,(01) .毕业设计（论文）应完成的主要工作：阅读中英文文献资料，熟练机动车号牌抓拍联网比对报警系统的组成，掌握比对原理及方法。毕业设计（论文）进度安排：序号毕业设计（论文）各阶段内容时间安排备注1资料的查阅、收集、整理，并撰写文献综述、开题报告、外文资料翻译12月5日1月20日确定基本设计方案2系统原理及方法研究1月21日2月10日3系统设计及整体调试2月11日3月15日4撰写毕业论文，整理相关表格资料3月16日4月20日5准备答辩课题信息：课题性质： 设计 论文 课题来源： 教学 科研 生产 其它发出任务书日期： 2011.12.6 指导教师签名： 2011年 12 月 6 日教研室意见：教研室主任签名：年 月 日 学生签名：中国地质大学长城学院本科毕业论文外文资料翻译系 别： 工程技术系 专 业： 机械设计制造及其自动化 姓 名： 学 号： 2012 年 3 月 20 日外文资料翻译译文基于人工神经网络的车牌照识别厄尔丁克kocera，kursatcevikbK摘要近年来，随着车辆数量在交通中的增加必要的个人工作在交通控制中的数量也随之增加。为了解决这个问题，计算机自动控制系统被开发。汽车牌照自动识别系统就是其中之一。在这个系统中，汽车牌照自动识别系统是基于人工神经网络的。在这个系统中，259个车辆图片被使用。这些车辆的图片是从相机中提取，然后车牌区域尺寸220x50的像素决定了这张照片使用的图像处理算法。字符包括字母和数字，在车牌定位中使用边缘检测算子和斑点的着色方法。斑点染色方法应用于ROL来区分车牌特征。在这一阶段的工作特征提取，采用平均绝对偏差公式。数字化特征进行分类使用前馈多层感知器神经网络回传播。关键词：车辆牌照识别，人工神经网络，模糊着色，字符识别1.简介在发展中国家，汽车数量日益增加。与此同时，必须认识到车辆和车牌同时也是增加的。以计算机为基础的车辆牌照自动识别系统为解决这一问题提供了必要性。在这项研究中，提出了一种高效的汽车牌照自动识别系统基于人工神经网络（神经网络）。该系统由三个主要议题：定位板地区的汽车图像，车牌字符图像的分割，字符分割和识别。该方案提出的车牌自动识别系统显示在图1。CameraVehicleFinding theSegmenting theFeatureRecognitionImagePlate RegionCharactersExtractionwith ANN图1 汽车牌照自动识别系统2.以前的作品根据土耳其民用车牌识别，及成功率（SR）为基础的车牌定位（PRL），字符分割（CS）和字符识别（CR）过程给出了表1 表1AuthorYearNumber of Image UsedSR for PRL (%)SR for CS (%)SR for CR (%)H. Caner20064292,8587,1794,12S. Ozbay200634097,6596,1898,82G. Yavuz200880929590B. Yalim200820096-92,53.定位车牌区域第一阶段的汽车牌照识别系统是找到车牌定位车辆图像。板区域通常由白底黑字组成。因此，过渡区之间的黑色和白色的颜色是非常密集的，在这一调查区域，包括大部分的过渡点，将足已定位车牌区域。为此，边缘检测算子应用于车辆的图像得到的过渡点。坎尼边缘检测器使用了一个过滤器的基础上的一阶导数的高斯平滑。经过平滑的形象，消除噪音，下一步就是提取图像的梯度。这一进程， 33矩阵被作为操作使用尺寸来进行边缘强度的梯度计算。这一信息使我们得到边缘点，如此密集的地区可确定过渡点。过渡点之间的黑色和白色的颜色确定了这一边缘图。边缘检测和定位车牌区域的图像显示在图2图2（一）原始汽车图像边缘检测；（二）局部区域4.字符分割灰度车牌图像分割过程之前应加强。因为对比度差异可能发生在提取图像的照相机。此外，不必要的肮脏的地区和一些噪音影响可以放在分割过程中负方向。在这项工作中，灰度图像的增强了运用对比的延伸和中值滤波技术。因此，对比差异图像和声音等脏区域在白色背景，该板可以消除。图像增强阶段后，斑点显色法的实施，确定边界的字符。4.1.对比度扩展扩展的图像对比度的手段，均衡直方图。换句话说，对比度扩展使图像锐化。灰度直方图是图像灰度分布值的图像。直方图均衡化是一种流行的技术，以改善外观形象差的对比。这个过程直方图均衡化的图像有4个步骤：（1）求直方图的值。（2）规范这些值除以总像素。（3）乘以这些正常价值的最大灰度值。（4）图的新的灰度值。对比度扩展车牌图像显示在图3。4.2.中值滤波中值滤波是用来消除不必要的噪音的地区。在这个滤波算法中周围的图像的33矩阵被截取。这个矩阵的尺寸可以根据噪声水平来进行调整。这个过程的工作，（1）一个像素为中心像素的33矩阵，（2）周围像素邻域像素分配，（3）排序过程之间采用这九个像素由小做大，（4）第五个元素分配为中位数元，（5）这些程序实施的所有像素图像。过滤后的图像显示在图3。图3（一）原车牌区域的图像；（二）对比度扩展图像；（三）中值滤波后的图像4.3.斑点的着色方法斑点（二进制大对象）着色算法具有很强的结构计算法来确定临近和相关地区二进制图像。该算法使用一种特殊形模板扫描，图像从左到右，从上到下。这种扫描过程确定独立的地区获得连接到四方向从零开始的背景图像。在这项工作中，四个方向的点着色算法应用于二进制编码的车牌图像获取字。实施后，字符分割得到了车牌区域的图像（图4）。图4字符分割在这项工作中，字符分割被列为单独的数字和字母。为此，板图像分为三个地区。第一区域包括双位数字，表明城市交通代码。二区域由一至三的字母。第三个区域由二至四位数字组成。板图像扫描形成确定自左向右水平和空间之间的字符。在这个过程，如果该值的空间是高于以前所鉴定的阈值。数字被定位为28x35像素大小。字母被定位为30x 40像素大小。样本的一些数字和字母分割的车牌区域显示在图5。图5一些样品的字符分割5.特征提取在这项研究中，获得的字符被保存为一个图像文件。数字的尺寸被确定为28x35像素，字母的尺寸被确定为30x 40像素。数字和字母进行单独的神经网络传输以提高识别的成功率。在分类之前，图像的特征应该被准确提取。特征提取能使我们获得直观的图像信息。这些信息可以作为一个特征向量。特征向量是包括全球和地方特点的一个字符编码，比较字符就可以确定特征。在拟议的方法，特征向量的虹膜图像编码使用平均绝对偏差算法。该算法的定义是：V1f (x, y) m(1)NNN是在图像的像素数，m是指图像的平均值，f（，y）是在点（，你）的值。在这项工作中，数字图像分为45像素尺寸的图像和字母图像分为55像素尺寸的图像。每个子图像进行特征提取采用AAD。我们得到特征向量长度49字节的数字和48字节的字母。整个特征向量应用到神经网络的输入进行分类提取特征。6.识别特征在我们的工作中，数字和字母进行单独的神经网络传输以提高识别的成功率。他们都有相同的结构，但只有输入数量差异。之所以使用不同的神经网络识别是防止类似的数字和字母被混淆，比如“0”和”O“啊”，“2”“Z”和“8”“B”。我们可以知道，这种复杂性将减少识别成功率。在拟议的方法，多层感知器模型是用于特征分类的。该处理单元被安排在MLP的多层感知器。这些输入层（包括信息，您可以使用作出决定），隐层（帮助网络计算更复杂的关系）和输出层（包括由此产生的决定）。每一个神经元的输入层是直接反馈到隐层神经元，在隐层，综合和产品的重量和隐层神经元输出计算每个输出层节点。如果错误计算输出值与期望值大于错误率，那么培训（改变重量和计算新的输出使用新的重量）过程开始。这个训练过程可以获得理想的错误率。训练神经网络，前馈反向传播算法选择。均方误差（均方差）的功能是用来测量培训网络性能的。值的均方差是用来确定如何适合网络输出所需的输出。标准监督训练通常是基于均方误差。培训计划终止时，均方误差下降到阈值。均方误差值接近零，计算输出值成为接近所需的输出值。7.实验结果为了评估系统的效能，259车辆图像被应用。快速BP学习算法用于训练神经网络。最大的5000次分别为每个输入设置。当系统达到最小错误率，由用户定义，迭代将停止。定义的最小错误率为该应用程序0001。只有一个输入图像是用于测试的系统，其余的是在训练阶段。iteration-mse图形的最佳结果为每个字符数据显示在图6。培训达到最小的误差率在4457个迭代的数量和1180个迭代的信件。图6均方误差迭代图形的训练过程成功率的车牌区域定位，字符分割和字符识别阶段，该系统在表2中给出了。因此，247个车牌图像被正确识别，所以整体识别率的系统是95,36%。表2。成功率的汽车牌照自动识别系统StageNumber of SamplesNumber of Correct ResultsSuccess Rate (%)PRL25925598,45CS25525298,82CR347 (Letters) + 1022 (Numbers)344 (Letters) + 1000 (Numbers)98,17外文原文Procedia Computer Science 3 (2011) 10331037/locate/procediaWCIT-2010Artificial neural networks based vehicle license plate recognitionH.Erdinc Kocera, K.Kursat CevikbaSelcuk Uni. Technical Education Faculty, Konya 42250, TurkeybNigde Uni. Bor Vocational High School, Nigde 51700, TurkeyAbstractIn recent years, the necessity of personal working in traffic control is increasing because the numbers of vehicles in traffic is increasing. To deal with this problem, computer based automatic control systems are being developed. One of these systems is automatic vehicle license plate recognition system. In this work, the automatic vehicle license plate recognition system based on artificial neural networks is presented. In this system, 259 vehicle pictures were used. These vehicle pictures were taken from the CCD camera and then the license plate region dimensioned by 220x50 pixels is determined from this picture by using image processing algorithms. The characters including letters and numbers placing in the license plate were located and determined by using Canny edge detection operator and the blob coloring method. The blob coloring method was applied to the ROI for separation of the characters. In the last phase of this work, the character features were extracted by using average absolute deviation formula. The digitized characters were then classified by using feed forward back propagated multi layered perceptron neural networks. The correct classification rates were given in last section.c 2010 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Guest Editor.Keywords: Vehicle licence plate recognition, artificial neural networks, blob coloring, character recognition.1. IntroductionWithin the fast developing countries, the number of vehicles is increasing day by day. In parallel to this, the need to recognize the vehicles and their license plates is increasing. To supply this necessity, computer based automatic vehicle license plate recognition systems are being developed recently. In this study, we proposed an efficient automatic vehicle license plate recognition system based on artificial neural networks (ANN). This system consists of three major topics. These are; localizing the plate region from the car image, segmenting the characters from the license plate image and recognizing the segmented characters. The block scheme of the proposed automatic license plate recognition system is shown in Fig 1.CameraVehicleFinding theSegmenting theFeatureRecognitionImagePlate RegionCharactersExtractionwith ANNFig. 1. The block scheme of an automatic license plate recognition systemThe layout of this work can be analyzed into 7 section. In section 1, the general information about this work was introduced, the previous works on license plate recognition was given as table in section 2. In section 3, localization1877-0509 c 2010 Published by Elsevier Ltd. doi:10.1016/cs.2010.12.1691034H. Erdinc Kocer, K. Kursat Cevik / Procedia Computer Science 3 (2011) 10331037H.Erdinc Kocer / Procedia Computer Science 00 (2010) 000000process was described. The segmentation process of the characters was introduced in section 4. In section 5, the feature extraction process of the segmented characters were presented. The recognition of the characters by using ANN was presented in Section 6. The experimental results were presented in the last section of this work.2. Previous WorksThe previous works will be held according to the works on Turkish civil license plate recognition. The success rates (SR) for the stage of plate region localization (PRL), character segmentation (CS) and character recognition (CR) processes were given in Table 1 1-6.Table 1. Success rates of the previous worksAuthorYearNumber of Image UsedSR for PRL (%)SR for CS (%)SR for CR (%)H. Caner20064292,8587,1794,12S. Ozbay200634097,6596,1898,82G. Yavuz200880929590B. Yalim200820096-92,5I. Irmakci200814596,5596,6195,25K. Bora2009225 (plate)-10089,333. Localization of the Plate RegionThe first stage of license plate recognition system is finding the plate location from vehicle image. The plate region consists of white background and black characters normally. Therefore, the transitions between black and white colors is very intensive in this region. Finding the region that includes most transition points would be adequate for localizing the plate region.For this purpose, Canny edge detection operator was applied to the vehicle images to get the transition points. The Canny edge detector uses a filter based on the first derivative of a Gaussian smoothing. After smoothing the image and eliminating the noise, the next step is to find the edge strength by taking the gradient of the image. For this process, this operator uses 3x3 dimensioned matrices. The edge strength of the gradient is then calculated. This information gives us the edge points, so the intensive transition points region can be determined. The transition points between black and white colors was then determined from this edge map. The original, the edge detected and the localized plate region images were shown in Fig 2a, 2b and 2c respectively.Fig. 2. (a) original car image; (b) edge detected; (c) localized plate region4. Segmentation of the CharactersThe gray level license plate image should be enhanced before segmentation process. Because the contrast differences can be occurred while taking the images by camera. Also, unwanted dirty regions can be placed on the plate and these noises affects the segmentation process in negative direction.In this work, the gray level plate images were enhanced by applying contrast extension and median filtering techniques. So, the contrast differences between images and the noises such as dirty regions in white background ofH. Erdinc Kocer, K. Kursat Cevik / Procedia Computer Science 3 (2011) 103310371035H.Erdinc Kocer/ Procedia Computer Science 00 (2010) 000000the plate can be eliminated. After image enhancement phase, the blob coloring method was implemented to determine the boundaries of the characters.4.1. Contrast extensionTo extend the contrast of an image means equalization of the histogram of that image. In other words, the contrast extension makes the image sharpen. The gray-level histogram of an image is the distribution of the gray level values in an image. The histogram equalization is a popular technique to improve the appearance of a poor contrasted image.The process of equalizing the histogram of an image consists of 4 steps 7: (1) Find the sum of the histogram values. (2) Normalize these values dividing by the total number of pixels. (3) Multiply these normalized values by the maximum gray-level value. (4) Map the new gray level values. The contrast extended license plate image is shown in Fig 3b.4.2. Median filteringMedian filter is used for eliminating the unwanted noisy regions. In this filtering method, the 3x3 matrices is passed around the image. The dimension of this matrices can be adjusted according to the noise level.The process is working as 7; (1) one pixel is chosen as center pixel of the 3x3 matrices, (2) the arounding pixels are assigned as neigborhood pixels, (3) the sorting process are employed between these nine pixels from smaller to the bigger, (4) the fifth element is assigned as median element, (5) these procedures are implemented to the all pixels in plate image. The filtered plate image is shown in Fig 3c.Fig. 3. (a) original plate region image; (b) contrast extended image; (c) median filtered image4.3. The blob coloring methodThe blob (Binary Large Object) coloring algorithm has a strong architecture to determine the closed and contacless regions in a binary image. This algorithm uses a special L shaped template to scan the image from left to right and from up to down. This scanning process determine the independent regions by obtaining the connections into four direction from zero valued background. In this work, four directional blob coloring algorithm is applied to the binary coding license plate image for getting the characters 8. After implementation, the segmented characters were obtained from the license plate region image (Fig 4).Fig. 4. The segmented charactersIn this work, the segmented characters were classified as numbers and letters separately. For this purpose, the plate image was divided into three region. The first region consists of two digit numbers that indicates the city traffic code. The second region consists of one-to-three digit letters. The third region consists of two-to-four digit numbers. The plate image was scanned form left to right horizontally and the spaces between characters were determined in this process. If the value of the space is higher than threshold value than the character region is signed. The numbers were localized as 28x35 pixels dimension. The letters were localized as 30x40 pixels dimension. Some samples of numbers and letters segmented from plate region are shown in Fig 5.Fig. 5. Some samples of the segmented characters1036H. Erdinc Kocer, K. Kursat Cevik / Procedia Computer Science 3 (2011) 1033103H.Erdinc Kocer / Procedia Computer Science 00 (2010) 000005. Feature ExtractionIn this study, the obtained characters were saved as an image file separately. The dimension of the numbers was determined as 28x35 pixels, the dimension of the letters was determined as 30x40 pixels. The numbers and the letters were classified by using two separate ANN for increasing the success rate of the recognition phase. Before classification, the character images should be feature extracted. Feature extraction provides us to obtain the most discriminating information of an image. This information can be presented as a feature vector. A feature vector that includes global and local features of an character should be encoded so that the comparison between characters can be made. In the proposed approach, the feature vector of an iris image was encoded by using Average Absolute Deviation algorithm 9. This algorithm is defined as:V1f (x, y) m(1)NNwhere N is the number of pixels in the image, m is the mean of the image and f(x,y) is the value at point (x,y). In this work, the number images were divided into 4x5 pixels dimensioned sub-images and the letter images were divided into 5x5 pixels dimensioned sub-images. Each sub-image were feature extracted by applying AAD. We obtained the feature vectors with the length of 49 byte for numbers and 48 byte for letters. The entire feature vectors were applied to the ANN as an input for classification of the characters.6. Recognition of the CharactersIn our work, the numbers and the letters were classified by using two separate ANN for increasing the success rate of the recognition phase. Both of them have same architecture but only the input numbers were differed. The reason for using two separate ANN for recognition is preventing the complexity of recognition of similar numbers and letters such as “0” “O”, “2” “Z” and “8” “B”. As we can know, this complexity will decrease the recognition success.In the proposed approach, a multi layered perceptron (MLP) ANN model was used for classification of the characters. The processing units in MLP are arranged in three layers. These are input layer (includes the information you would use to make decision), hidden layer (helps network to compute more complicated associations) and output layer (includes the resulting decision) 10,11. Each neuron in the input layer is fed directly to the hidden layer neurons via a series of weights. The sum of the products of the weights and the inputs is calculated in each node. The calculated values are fed directly to the output layer neurons via a series of weights. As in hidden layer, the sum of the products of the weights and the hidden layer neuron outputs is calculated in each node in the output layer. If the error between calculated output value and the desired value is more than the error ratio, then the training (changing the weights and calculating the new output by using the new weights) process begins. This training process can be finished by obtaining the desired error rate for all input combinations.For training the ANN, feed-forward back-propagation algorithm was chosen. For measuring the training performance of the network, mean square error (MSE) function is used. The value of the MSE is used to determine how well the network output fits the desired output. The stop criteria for supervised training are usually based on MSE. Most often the training is set to terminate when the MSE drops to some threshold. Approaching the MSE value to the zero means that the calculated output value is becoming the closer to the desired output value.7. Experimental ResultsIn order to evaluate the performance of the proposed system, 259 vehicle images were employed. Sigmoidfunction is used in the activation of neurons. Quick back propagation learning algorithm was used for training the ANN. Maximum 5000 iterations were performed for each input set. When the system reach to the minimum error rate which defined by the user, the iterations will be stopped. The defined minimum error rate for this application is 0,001. Only one of the input images was used for testing the system, the rest was performed in training phase. The iteration-MSE graphics of best results for each character data set are shown in Fig 6. The training reaches the minimum error rate in 4457 iterations for the num
- 温馨提示:
1: 本站所有资源如无特殊说明，都需要本地电脑安装OFFICE2007和PDF阅读器。图纸软件为CAD,CAXA,PROE,UG,SolidWorks等.压缩文件请下载最新的WinRAR软件解压。
2: 本站的文档不包含任何第三方提供的附件图纸等，如果需要附件，请联系上传者。文件的所有权益归上传用户所有。
3.本站RAR压缩包中若带图纸，网页内容里面会有图纸预览，若没有图纸预览就没有图纸。
4. 未经权益所有人同意不得将文件中的内容挪作商业或盈利用途。
5. 人人文库网仅提供信息存储空间，仅对用户上传内容的表现方式做保护处理，对用户上传分享的文档内容本身不做任何修改或编辑，并不能对任何下载内容负责。
6. 下载文件中如有侵权或不适当内容，请与我们联系，我们立即纠正。
7. 本站不保证下载资源的准确性、安全性和完整性, 同时也不承担用户因使用这些下载资源对自己和他人造成任何形式的伤害或损失。
人人文库网所有资源均是用户自行上传分享，仅供网友学习交流，未经上传用户书面授权，请勿作他用。
川公网安备: 51019002004831号