【摘要】：隨著生產(chǎn)技術(shù)的進步,消費者的需求日益提高。這種需求不僅意味著需求量的增加,更有需求品質(zhì)的提升。質(zhì)量管理是現(xiàn)代化工業(yè)生產(chǎn)提高市場競爭優(yōu)勢的一個重要方法。在現(xiàn)代化工業(yè)生產(chǎn)過程中,穩(wěn)定的工藝流程是影響產(chǎn)品質(zhì)量的一個重要因素。統(tǒng)計過程控制中的質(zhì)量控制圖,常被用來監(jiān)控產(chǎn)品質(zhì)量的穩(wěn)定性。然而傳統(tǒng)的控制圖已不再適應現(xiàn)代化大生產(chǎn)的需求。借助于先進的計算機信息處理技術(shù),把人工智能技術(shù)應用工業(yè)過程控制中去,實現(xiàn)工業(yè)過程中質(zhì)量控制的實時性、準確性是當前國內(nèi)外專家學者研究的方向之一。本文總結(jié)了在現(xiàn)代化工業(yè)生產(chǎn)過程中,關(guān)于質(zhì)量管理領(lǐng)域的控制圖模式識別的國內(nèi)外研究現(xiàn)狀和發(fā)展趨勢,介紹了統(tǒng)計過程控制的基本概念和質(zhì)量控制圖的基本原理,對本文涉及的控制圖的判定原則、神經(jīng)網(wǎng)絡(luò)的基本理論及其泛化和集成理論、協(xié)同進化等相關(guān)理論進行了闡述和分析,給本文研究的開展提供了理論支撐。通過分析目前質(zhì)量控制圖模式識別方法中存在的不足和缺陷,結(jié)合人工神經(jīng)網(wǎng)絡(luò)在處理復雜分類問題方面的特點,利用協(xié)同進化的思想提出了一種神經(jīng)網(wǎng)絡(luò)集成的設(shè)計和訓練的方法。通過對神經(jīng)網(wǎng)絡(luò)集成泛化誤差的分析,將神經(jīng)網(wǎng)絡(luò)學習算法和協(xié)同進化算法相結(jié)合,用個體網(wǎng)絡(luò)的相關(guān)度度量網(wǎng)絡(luò)集成的誤差從而實現(xiàn)個體網(wǎng)絡(luò)的差異性,個體神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu)在學習過程中自動確定,保持了個體網(wǎng)絡(luò)的準確性,通過構(gòu)造方法自動確定神經(jīng)網(wǎng)絡(luò)集成的結(jié)構(gòu),提高了集成學習系統(tǒng)的穩(wěn)定性和泛化能力。最后利用蒙特卡羅質(zhì)量特征數(shù)據(jù)模擬方法生成與實際生產(chǎn)過程相似的質(zhì)量特征序列,運用MATLAB2012a對控制圖6種基本模式識別網(wǎng)絡(luò)進行編程訓練,仿真結(jié)果表明所訓練的神經(jīng)網(wǎng)絡(luò)集成CNNE模型具有很強的識別能力其性能明顯優(yōu)于BP網(wǎng)絡(luò)和RBF網(wǎng)絡(luò)等單個的神經(jīng)網(wǎng)絡(luò)分類方法,也優(yōu)于Bagging和Adaboost等傳統(tǒng)的集成方法。
[Abstract]:With the progress of production technology, the demand of consumers is increasing day by day. This demand not only means the increase of demand, but also the improvement of demand quality. Quality management is an important method to improve the competitive advantage of modern industrial production. In the process of modern industrial production, stable technological process is an important factor affecting product quality. The quality control chart in statistical process control is often used to monitor the stability of product quality. However, the traditional control chart is no longer suitable for the needs of modern mass production. With the help of advanced computer information processing technology, artificial intelligence technology is applied to industrial process control to realize the real-time quality control in industrial process. Accuracy is one of the current research directions of experts and scholars at home and abroad. This paper summarizes the research status and development trend of control chart pattern recognition in the field of quality management at home and abroad in the process of modern industrial production, introduces the basic concept of statistical process control and the basic principle of quality control chart, and expounds and analyzes the decision principle of control chart, the basic theory of neural network and its generalization and integration theory, co-evolution and so on. It provides theoretical support for the development of this paper. By analyzing the shortcomings and defects of the current quality control chart pattern recognition methods, combined with the characteristics of artificial neural network in dealing with complex classification problems, a neural network integration design and training method is proposed by using the idea of co-evolution. Through the analysis of the generalization error of neural network integration, the neural network learning algorithm and co-evolution algorithm are combined, and the correlation degree of individual network is used to measure the error of network integration so as to realize the difference of individual network. The structure of individual neural network is determined automatically in the learning process, which maintains the accuracy of individual network, and the structure of neural network integration is determined automatically by construction method. The stability and generalization ability of the integrated learning system are improved. Finally, Monte Carlo quality feature data simulation method is used to generate quality feature sequences similar to the actual production process, and MATLAB2012a is used to program and train six basic pattern recognition networks in control chart. The simulation results show that the trained neural network integrated CNNE model has strong recognition ability, and its performance is obviously better than that of BP network and RBF network, as well as the traditional integration methods such as Bagging and Adaboost.
【學位授予單位】：中北大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP183

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