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这是一个用matlab实现的RBF神经网络手写数字识别算法.此算法加入相应的手写数字图后可以运行.-This is a realization of using Matlab RBF neural network recognition algorithm handwritten figures. This algorithm adherence to the corresponding figures handwritten map after the run.
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模式识别中的脱机字符识别，包括手写数字识别之Fisher线性判别，手写数字识别之模板匹配法，数字识别之神经网络法及细化算法。-pattern recognition of Offline Character Recognition, including handwritten digital identification Fisher Linear Discriminant. Handwritten identification template matching, digital identi
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神经网络进行手写数字识别：
本程序是BP算法的演示程序, 其中的Levenberg-Marquardt算法具有实用价值.
带有图形界面-neural network handwritten numeral recognition : this program is the BP algorithm Demonstration Program, The Levenberg-Marquardt algorithm is practical value. with a graphical in
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脱机字符识别算法，包括手写数字识别之Fisher线性判别，手写数字识别之模板匹配法，数字识别之神经网络法，细化算法
-offline character recognition algorithms, including handwritten digital identification Fisher Linear Discriminant. Handwritten identification template matching, digital identification neura
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基于神经网络的手写数字识别的源代码，绝对能够正常编译并运行！-based on neural network handwritten numeral recognition of the source code is absolutely normal to compile and run!
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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,
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基于BP神经网络的手写体数字识别程序，使用matlab开发-based Neural Network handwritten numeral recognition, the use of Matlab development
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用于手写体字符识别的BP神经网络算法,用C语言编写,需要用一定数据的进行训练,然后用三层网络进行识别,可以试一试.-for Handwritten Character Recognition BP neural network algorithm, using C language, need certain data for training, and then use the three-tier network identification, may try.
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基于自组织神经网络（自组织特征映射）的手写数字识别原代码-Based on self-organizing neural network (SOFM) Handwritten Digit Recognition of the original code
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脱机字符识别算法，包含手写数字识别之Fisher线性判别，手写数数字识别之模板匹配法，数字识别之神经网络法，细化算法 已通过测试。
-Off-line character recognition algorithm, contains handwritten numeral recognition of the Fisher linear discriminant, the template matching method for handwritten numerals recogniti
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用Skeletonization剪枝方法精简BP神经网络结构，提高网络泛化能力，对手写数字进行识别。-Skeletonization pruning method using BP neural network architecture to streamline and improve the network generalization ability of the handwritten digits recognition.
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一个基于神经网络的手写体数字识别的matlab程序，可以自行进行神经网络训练并识别给出相应的结果-A neural network-based handwritten numeral recognition matlab program, the neural network can be trained to identify themselves and give the corresponding results
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为了实现对手写字体的识别，运用了人工智能的分层神经网络思想，对识别的字体通过训练学习，达到识别手写字体的功能。-to realize the recognition of handwritten font, using hierarchical neural network artificial intelligence, to identify fonts through training and learning, to identify the handwriting function.
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基于Bayes和神经网络的手写体数字识别程序(matlab)-Handwritten numeral recognition program based on Bayes and neural network(matlab)
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神经网络的手写 数字识别matlab。包含数据库，可直接运行。-Handwritten numeral recognition matlab neural network.Contains a , can be directly run
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一种基于RBF、BP神经网络手写数字识别的方法，里面包含GUI界面，方便理解，代码注释详细-One kind of RBF, BP neural network handwritten numeral recognition based method, which contains GUI interface, easy to understand, detailed code comments
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CNN learning 手写数字识别 神经网络-CNN learning handwritten numeral recognition neural network
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本文主要实现手写数字识别，利用多类逻辑回归与神经网络两种方法实现,并编写有GUI界面。(This paper mainly implements handwritten numeral recognition, using multiple logic regression and neural network to achieve two methods, and the preparation of a GUI interface.)
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由java编写的，具有gui界面的，手写数字识别神经网络示例(Written by Java, with GUI interface, handwritten numeral recognition neural network examples)
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识别手写数字0~9，包括如何获取数字特征，如何将图形数字转换为0，1的方阵(Recognition of handwritten digits 0~9)
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