Description: 论文标题:自适应模糊系统在手写体数字识别中的应用研究
作者:张镭
作者专业:计算机软件人工智能
导师姓名:黄战
授予学位:硕士
授予单位:暨南大学
授予学位时间:19990501
论文页数:59页
文摘语种:中文文摘
分类号:TP18 TP391.4
关键词:手写体数字 自适应 模糊逻辑 神经网络 模式识别
摘要:该文针对模式识别的特点,构造了适合于模式识别问题的自适应模糊系统,对三种不同学习算法加以改进,在手写全数字识别上对分类器进行了实现,并与采用BP算法训练的三层前馈神经网络分类器相比较,分析其优劣.仿真实验表明,在该文的样本集条件下,自适应模糊分类吕的识别性能优于神经网络分类器,这充分体现了自适应模糊技术用于数字识别的优越性和潜力.-thesis entitled : adaptive fuzzy system in a handwritten numeral recognition of applied research Author : Zhang Lei professional author : artificial intelligence computer software instructor Name : Huang war conferred degrees : Master award units : Jinan University conferred degrees : 19990501 page thesis : Abstracts 59 languages : Chinese Digest Key words : TP18-image Keywords : handwritten digital adaptive fuzzy logic neural network pattern recognition Abstract : In this paper the characteristics of pattern recognition, suitable for the construction of adaptive pattern recognition fuzzy systems, three different learning algorithm to improve the handwriting recognition on digital classification of the device to achieve, BP and with a three-tiered training algorithm for neural network clas Platform: |
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Author:成东 |
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Description: 这程序是《神经网络模式识别及其实现》（Pattern Recognition with Neural Networks in C++)美Abhijit S. Pandya等著电子工业出版社1999年6月
书号ISBN 7-5053-5088-9-This process is a Platform: |
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Author:罗新斌 |
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Description: 自己写的matlab源程序，神经网络原理与模式识别上的一个作业题，关于对数字1～9的识别的，用BP神经网络matlab工具箱来做的。-Their own source code written in matlab, neural network theory and pattern recognition operation on a title, with respect to figures 1 to 9 of identification, using BP neural network toolbox matlab to do the. Platform: |
Size: 8192 |
Author:wangdenuan |
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Description: Visual C++数字图像模式识别技术及工程实践(随书光盘）人民邮电出版社2003张宏林本书介绍了模式识别和人工智能中的一些基本理论以及一些相关的模型,包括贝叶斯决策、线性判别函数、神经网络理论、隐马尔可夫模型、聚类技术等,同时结合模式识别中的一些经典问题,从多种不同的角度介绍了这些问题的解决思路。-Visual C++ Digital Image Pattern Recognition Technology and Engineering Practice (Book with CD-ROM)张宏林Posts & Telecom Press, 2003 book of pattern recognition and artificial intelligence, introduced some of the basic theory and some related models, including Bayesian decision-making, Linear Discriminant function, neural network theory, hidden Markov models, clustering technologies, combined with some of the classic pattern recognition problems, from the perspective of a variety of introduced ideas to solve these problems. Platform: |
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Author:swallow |
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Description: 这程序是《神经网络模式识别及其实现》（Pattern Recognition with Neural Networks in C++)美Abhijit S. Pandya等著电子工业出版社1999年6月书号ISBN 7-5053-5088-9的第九章<神经联想记忆和Hopfield网络>附的程序-This code in this directory implements the binary hopfield network.
Source code may be found in HOPNET.CPP. A sample training file is
H7x8N4.trn. Sample test pattern files are: H7x8D4.TST, H5x8D7.TST,
H5x8D7.TST and H5x8D9.TST, Output of the program goes to both the
screen and a file, ARCHIVE.LST. Platform: |
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Author:yanghy |
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Description: Precise and fast control chart pattern (CCP) recognition is important for monitoring process environments
to achieve appropriate control and to produce high quality products. CCPs can exhibit six types of
pattern: normal, cyclic, increasing trend, decreasing trend, upward shift and downward shift. Except for
normal patterns, all other patterns indicate that the process being monitored is not functioning correctly
and requires adjustment. This paper describes a new type of neural network for speeding up the training
process and to compare three training algorithms in terms of speed, performance and parameter complexity
for CCP recognition. The networks are multilayered perceptrons trained with a resilient propagation,
backpropagation (BP) and extended delta-bar-delta algorithms. The recognition results of CCPs show the
BP algorithm is accurate and provides better and faster results. Platform: |
Size: 374784 |
Author:singh |
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Description: 图象处理 模式识别 多种分类方法(最临近匹配分类器、Bayes分类器、线性函数分类、非线性函数分类、神经网络分类)识别0-9数字 手写数字与数字图片,包括设计训练样品库、可以选择多种分类器来识别识别0-9这十个阿拉伯数字，包括临时手写的数字，也包括图片中的数字
-Pattern recognition image processing a variety of classification (the most close to matching classifier, Bayes classifier, a linear function of classification, non-linear function of classification, neural network classification) to identify any number of handwritten digits 0-9 and digital images, including the design of training samples library, you can choose a variety of classifier to identify the 10 Arabic numerals identify the 0-9, including temporary handwritten figures also include the number of pictures Platform: |
Size: 541696 |
Author:yao |
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Description: 《精通VisualC++数字图像处理模式识别技术及工程实践》介绍了模式识别和人工智能中的一些基本理论，以及一些相关的模型，包括贝叶斯决策、线性判别函数、神经网络理论、隐马尔可夫模型、聚类技术等，同时结合模式识别中的一些问题，比如字符识别、笔迹鉴定、人脸检测、车牌识别、印章识别以及遥感图片、医学图片处理等内容，从多种角度，介绍了解决这些问题的思路-" Proficient in VisualC++ digital image processing, pattern recognition technology and engineering practice," describes the pattern recognition and artificial intelligence in some of the basic theory, and a number of related models, including Bayesian decision-making, linear discriminant function, neural network theory, hidden Markov Cardiff model, clustering technologies, combined with pattern recognition in a number of issues, such as character recognition, handwriting identification, face detection, license plate recognition, seals, and remote-sensing image recognition, medical image processing and other content, from several angles, introduced ideas to solve these problems Platform: |
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Author:吴晶 |
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Description: this code for character recognition.this code can recognize an image with array matrix of [3x3]. this code apply a neural network method.-this is code for character recognition.this code can recognize an image with array matrix of [3x3]. this code apply a neural network method. Platform: |
Size: 1024 |
Author:wanfazri |
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Description: 分章节给出了Pattern Recognition with Neural Networks in C++（C++在模式识别与神经网络应用）的代码，对于初学者有较好的借鉴作用-Sub-sections given Pattern Recognition with Neural Networks in C++ (C++ code in pattern recognition and neural network application) for beginners have a good reference Platform: |
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Author:lisong |
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Description: BP神经网络用与实现不同故障的分类，对复杂信号模式识别是一种很有效的方法。-BP neural network with the classification of different faults and implementation of complex signal pattern recognition is a very effective method. Platform: |
Size: 1024 |
Author:真人 |
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Description: Fuzzy system is good at express people s experiential
knowledge. It can deal with vague information. It can solve the
intelligent questions better. Fuzzy clustering methods have
been used widely in pattern recognition. Combine fuzzy
systems with genetic-neural Network Algorithm not only make
the algorithm more efficient, but also to address the intelligent
questions better. It has become a hot research. Platform: |
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Author:feiminyu |
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Description: As signatures are widely accepted bio-metric for authentication and identification of a person because every person
has a distinct signature with its specific behavioral property, so it is very much necessary to prove the authenticity of signature
itself. There are various techniques to signature recognition with a lot of scope of research. In this paper off-line signature
recognition and verification system using Artificial Neural Network (ANN) is purposed. The purposed network based upon the
adaption of ANN to recognized signature to connected type pattern. The purpose ANN was trained with back propagation with
momentum and adaptive learning rate. A triple hidden layer ANN with 100 inputs 58.38.20 hidden neurons layers and 5 neurons
in output layers gives best results as compared with other networks. This paper represents a brief review on various approaches
used in signature verification systems. Platform: |
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Author:shiwi |
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Description: 这段代码实现了一个新的MLP神经网络训练方法,来自论文A new method for neural network regularization（内附）-This code implements a new training method for MLP neural networks, named Support Vector Neural Network (SVNN), proposed in the work: O. Ludwig “Study on Non-parametric Methods for Fast Pattern Recognition with Emphasis on Neural Networks and Cascade Classifiers ” PhD Thesis, University of Coimbra, Coimbra, 2012. The input arguments are a N x L matrix of L representative N-element input vectors, a row vector, y, whose elements are the respective target classes, which should be-1 or 1, and the number of hidden neurons, nneu. Similarly to SVMs, the SVNN has a punishing parameter, C, which can be set in the line 16 of the code. The algorithm outputs the MLP parameters, W1, W2, b1, b2, which are input arguments of the MLP simulator “sim_NN.m” that also requires the matrix of testing data, as well as the target vector (in case of target unavailable, a empty vector must be supplied). “sim_NN.m” outputs the estimated class and the accuracy, acc (when testing targets are available). The code Platform: |
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Author:孙园 |
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Description: 本书注重对数学分析方法和性能优化的讨论，强调神经网络在模式识别、信号处理和控制 系统等实际工程问题中的应用。书中包含大量例题和习题，并配有13个基于MATLAB软件的计算 机实验程序。-The book focuses on mathematical analysis methods and performance optimization of the discussion, emphasizing neural network in pattern recognition, signal processing and control systems and other practical engineering problems in the application. The book contains a large number of examples and exercises, and is equipped with 13 MATLAB software-based computer experimental program. Platform: |
Size: 18510848 |
Author:张杰 |
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Description: 对植物的分类研究已经突破了单纯从植物细胞及化学遗传成分的角度去鉴 定植物种类的方法, 可以综合应用图像处理技术和模式识别技术 , 辅以图像获取设备实现对植物的快速识别 。 为 此 , 精心选取了植物叶片图像的典型形状特征 , 构成了叶片识别的特征向量, 然后用概率神经网络 (P NN)作为分类 器 , 对样本进行训练。 实验结果证明 , 针对少量常见的植物叶片图像, PNN与 BP神经网络相比有更好的识别效率 -Research on the classification of the plant has been broke through the simple the Angle of plant cell and chemical genetic component to guide The method of plant species, can be integrated application of image processing and pattern recognition technology, supplemented by image acquisition device to realize fast recognition of plants.For this, carefully the typical shape characteristics of the plant leaf image, form the leaf recognition feature vector, then using probabilistic neural network (NN) P as a classifier, the training samples.The experimental results show that for a few common plant leaf image, PNN compared with BP neural network has better recognition efficiency Platform: |
Size: 300032 |
Author:hahah |
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Search - Pattern Recognition with a Neural Network