神經(jīng)網(wǎng)絡(luò)在模式識(shí)別中的簡(jiǎn)單分析及應(yīng)用

電子通信論文編號(hào):TX269  論文字?jǐn)?shù):16622,頁(yè)數(shù):47

目  錄
引言 - 1 -
1  模式識(shí)別概述 - 1 -
1.1 模式識(shí)別基本概念 - 1 -
1.2 模式識(shí)別系統(tǒng) - 2 -
1.3 模式識(shí)別的主要方法 - 2 -
1.4 模式識(shí)別應(yīng)用 - 3 -
2  人工神經(jīng)網(wǎng)絡(luò)概述 - 4 -
2.1 人工神經(jīng)元模型 - 4 -
2.1.1閥值函數(shù)、Sigmoid函數(shù)和分線段性函數(shù) - 5 -
2.2 人工神經(jīng)網(wǎng)絡(luò)模型 - 5 -
2.3 神經(jīng)網(wǎng)絡(luò)學(xué)習(xí)特點(diǎn) - 6 -
2.4 人工神經(jīng)網(wǎng)絡(luò)在模式識(shí)別問題中應(yīng)用優(yōu)勢(shì) - 6 -
3  神經(jīng)網(wǎng)絡(luò)模式識(shí)別 - 7 -
3.1 基于BP神經(jīng)網(wǎng)絡(luò)的模式識(shí)別 - 7 -
3.1.1BP神經(jīng)網(wǎng)絡(luò)模型簡(jiǎn)述 - 7 -
3.1.2BP學(xué)習(xí)算法 - 8 -
3.1.3BP神經(jīng)網(wǎng)絡(luò)應(yīng)用于字符識(shí)別 - 9 -
3.2 基于徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)的模式識(shí)別 - 16 -
3.2.1徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)模型簡(jiǎn)述 - 17 -
3.2.2徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)應(yīng)用特征模式的分類 - 18 -
3.3 基于自組織競(jìng)爭(zhēng)神經(jīng)網(wǎng)絡(luò)的模式識(shí)別 - 19 -
3.3.1自組織競(jìng)爭(zhēng)神經(jīng)網(wǎng)絡(luò)基本思想 - 20 -
3.3.2自組織競(jìng)爭(zhēng)神經(jīng)網(wǎng)絡(luò)設(shè)計(jì)模式分類器的應(yīng)用 - 20 -
3.4 基于反饋型神經(jīng)網(wǎng)絡(luò)的模式識(shí)別 - 24 -
3.4.1反饋型神經(jīng)網(wǎng)絡(luò)模型簡(jiǎn)述 - 24 -
3.4.2反饋型神經(jīng)網(wǎng)絡(luò)在工程領(lǐng)域的應(yīng)用 - 24 -
4  實(shí)驗(yàn)分析與總結(jié) - 31 -
參考文獻(xiàn) - 32 -
附  錄 - 33 -

摘  要

 模式識(shí)別就是機(jī)器識(shí)別、計(jì)算機(jī)識(shí)別或者機(jī)器自動(dòng)化識(shí)別，目的在于讓機(jī)器自動(dòng)識(shí)別事物，使機(jī)器能做以前只能由人類才能做的事，具備人所具有的對(duì)各種事物與現(xiàn)象進(jìn)行分析、描述與判斷的部分能力。它研究的目的就是利用計(jì)算機(jī)對(duì)物理對(duì)象進(jìn)行分類，在錯(cuò)誤概率最小的條件下，使識(shí)別的結(jié)果盡量與客觀事物相符合。
 隨著人們對(duì)人工神經(jīng)網(wǎng)絡(luò)的不斷地認(rèn)識(shí)，神經(jīng)網(wǎng)絡(luò)是指用大量的簡(jiǎn)單計(jì)算單元構(gòu)成的非線性系統(tǒng),它在一定程度和層次上模仿了人腦神經(jīng)系統(tǒng)的信息處理、存儲(chǔ)及檢索功能，因而具有學(xué)習(xí)、記憶和計(jì)算等智能處理功能。這樣人們利用人工神經(jīng)網(wǎng)絡(luò)具有高度的并行性，高度的非線性全局作用以及良好的容錯(cuò)性與聯(lián)想記憶功能，并且具有良好的自適應(yīng)、自學(xué)習(xí)功能等突出特點(diǎn)，可運(yùn)用MATLAB神經(jīng)網(wǎng)絡(luò)工具箱中的神經(jīng)網(wǎng)絡(luò)模型，對(duì)經(jīng)過訓(xùn)練的神經(jīng)網(wǎng)絡(luò)可以有效地提取信號(hào)、語(yǔ)音、圖像等感知模式的特征，并能解決現(xiàn)有啟發(fā)式模式識(shí)別系統(tǒng)不能很好解決的不變量探測(cè)、抽象和概括等問題。這樣神經(jīng)網(wǎng)絡(luò)可應(yīng)用于模式識(shí)別的特征提取、聚類分析、邊緣檢測(cè)、信號(hào)增強(qiáng)以及噪聲抑制、數(shù)據(jù)壓縮等各個(gè)環(huán)節(jié)。使用機(jī)器來(lái)進(jìn)行模式的識(shí)別是一項(xiàng)非常有用的工作，能夠辨別符號(hào)等系列的機(jī)器是很有價(jià)值的。目前，模式識(shí)別技術(shù)可以應(yīng)用指紋識(shí)別、IC卡技術(shù)應(yīng)用、字符識(shí)別等實(shí)例。模式識(shí)別成為人工神經(jīng)網(wǎng)絡(luò)特別適宜求解的一類問題。因此，神經(jīng)網(wǎng)絡(luò)技術(shù)在模式識(shí)別中也得到廣泛應(yīng)用與發(fā)展。
 
關(guān)鍵詞：模式識(shí)別；人工神經(jīng)網(wǎng)絡(luò)；神經(jīng)網(wǎng)絡(luò)模型；神經(jīng)網(wǎng)絡(luò)技術(shù)

Abstract

 Pattern Recognition is the machine identification, computer identification or identification of machine automation, machine aimed at automatic identification of things to do before the machine can only be made by man can do, with people with all kinds of things and on an analysis of the phenomenon, described with the ability to determine the part. It is the purpose of the study of the physical object to use the computer for classification, the probability of the smallest in the wrong conditions, so that the results of recognition as far as possible in line with objective things.
 As artificial neural network to recognize the continuing, neural network refers to a large number of simple calculation unit consisting of non-linear system, which to some extent and level system to imitate the human brain's information processing, storage and retrieva l functions, which has learning, memory and computing functions such as intelligent processing. Such people to use artificial neural network with a high degree of parallelism, the overall role of a high degree of non-linear and good fault tolerance and associative memory function, and have good self-adaptive, self-learning function, such as prominent features, the availability of MATLAB neural network toolbox The neural network model trained neural network can effectively extract the signal, voice, video and other features of perceptual patterns and heuristics to solve the existing pattern recognition systems are not well resolved invariant detection, such as abstract and summary issues. This neural network pattern recognition can be applied to feature extraction, clustering analysis, edge detection, signal enhancement and noise suppression, data compression, such as various links. The use of machines for pattern recognition is a very useful work, such as series of symbols to identify the machines are of great value. At present, the pattern recognition technology can be applied to fingerprint identification, IC card technology applications, such as examples of character recognition. Artificial neural network pattern recognition has become especially suitable for solving a class of problem. Therefore, the neural network pattern recognition technology is also widely used and development.
 
Key words：pattern recognition；artificial neural network；neural network model；neural network technology