測(cè)控論文編號(hào):CK045   論文字?jǐn)?shù):21735,頁(yè)數(shù):41

目 錄
摘要…………………………………………………………………………………………..II
1 緒論………………………………………………………………………………………..1
2總體設(shè)計(jì)方案…………………………………………………………………………….2
   2.1 主要任務(wù)與目標(biāo)……………………………………………………………………..2
   2.2 主要內(nèi)容與要求………………………………………………………………………2
   2.3 設(shè)計(jì)思路簡(jiǎn)介…………………………………………………………………………2
3 BP神經(jīng)網(wǎng)絡(luò)……………………………………………………………………………. …….3
   3.1神經(jīng)網(wǎng)絡(luò)概述…………………………………………………………………….. ….3
   3.2 神經(jīng)網(wǎng)絡(luò)的基本特征…………………………………………………………………3
   3.3 神經(jīng)網(wǎng)絡(luò)的基本功能…………………………………………………………………4
   3.4神經(jīng)網(wǎng)絡(luò)的發(fā)展概況…………………………………………………………………5
   3.5 神經(jīng)元模型……………………………………………………………….. ….. ….. …6
   3.6 神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu)…………………………………………………….. ….. ….. ….. ….8
         3.7 神經(jīng)網(wǎng)絡(luò)的訓(xùn)練方法………………………………………………………………… 10
 3.8 BP神經(jīng)網(wǎng)絡(luò)的基本原理 …………………………………………………….. ….. …11
   3.9 BP神經(jīng)網(wǎng)絡(luò)的算法實(shí)現(xiàn)及原理框圖…………………………………………………12
   3.10網(wǎng)絡(luò)結(jié)構(gòu)參數(shù)的選取…………………………………………………………………..13
   3.11 BP神經(jīng)網(wǎng)絡(luò)的特點(diǎn)和局限性…………………………………………………………14
4 字符識(shí)別技術(shù)的基本理論……………………………………………………………..15
   4.1模式識(shí)別簡(jiǎn)介…………………………………………………………………………..15
   4.2模式識(shí)別方法的比較……………………………………………………………………15
   4.3字符識(shí)別技術(shù)簡(jiǎn)介………………………………………………………………………16
   4.4字符識(shí)別技術(shù)的分類……………………………………………………………………17
   4.5當(dāng)前的字符識(shí)別技術(shù)研究熱點(diǎn)及國(guó)內(nèi)外概況…………………………………………17
5 編程工具M(jìn)ATLAB及神經(jīng)網(wǎng)絡(luò)工具箱………………………………………………19
6 圖形用戶界面………………………………………………………………………………21
   6.1 簡(jiǎn)介………………………………………………………………………………………21
   6.2 可視化的界面環(huán)境………………………………………………………………………21
7 基于MATLAB的神經(jīng)網(wǎng)絡(luò)設(shè)計(jì)與仿真………………………………………………24
   7.1 神經(jīng)網(wǎng)絡(luò)字符識(shí)別系統(tǒng)的設(shè)計(jì)總況……………………………………………………24
   7.2 GUI界面設(shè)計(jì)……………………………………………………………………………26
     致謝…………………………………………………………………………………………36
參考文獻(xiàn)………………………………………………………………………………………..37

摘  要
 神經(jīng)網(wǎng)絡(luò)模式識(shí)別方法是近幾年興起的模式識(shí)別領(lǐng)域的一個(gè)新的研究方向。由于神經(jīng)網(wǎng)絡(luò)的高速并行處理、分布存貯信息等特性符合人類視覺(jué)系統(tǒng)的基本工作原則．具有很強(qiáng)自學(xué)習(xí)性 、組織性、容錯(cuò)性 、高度非線性、高度魯棒性、聯(lián)想記憶功能和推理意識(shí)功能。本文首先介紹了神經(jīng)網(wǎng)絡(luò)的基本概念，特別是BP神經(jīng)網(wǎng)絡(luò)的原理和基本算法。同時(shí)介紹了模式識(shí)別的各種方法，并進(jìn)一步了解了字符識(shí)別技術(shù)的基本理論。主要討論用人工神經(jīng)網(wǎng)絡(luò)方法對(duì)英文字符的識(shí)別，將標(biāo)準(zhǔn)印刷體英文字符進(jìn)行二值化處理，用矩陣表示，從而進(jìn)行識(shí)別。同時(shí)考慮了噪聲干擾或者是非線性因素的存在。分別用理想樣本和混有噪聲的樣本訓(xùn)練神經(jīng)網(wǎng)絡(luò)并繪出誤差曲線進(jìn)行比較。再者通過(guò)設(shè)置回調(diào)函數(shù)利用GUI界面開(kāi)發(fā)字符識(shí)別系統(tǒng)，使其直觀顯示。
關(guān)鍵詞 神經(jīng)網(wǎng)絡(luò);字符識(shí)別  
Abstract
 Neural networks method is a new method in recent pattern recognition; it provides a new means to Character Recognition. It has the advantage over some traditional technology: Good tolerance, Classification capability, Parallel processing capacity and self-learning ability. Thus, the neural network identification is a good choice.
 The passage introduces all kinds of methods of character recognition, mainly discuss using Artificial Neural Network method to recognize character, and consider the existence of noise disturbance or nonlinear factor, so the network has certain fault-tolerance capability, and finally use MATLAB to simulate character recognition.
 The study focuses on the standard Print English character recognition. Union pattern recognition and artificial neural network algorithm for a large number of related, especially feed-forward neural network model which is the most useful in target identification at present and the BP algorithm, and making use of GUI platform to develop the character recognition system in the end.

Keywords Neural Network ;Character Recognition