本文選題：板形識別 + 混沌免疫遺傳算法��； 參考：《濟(jì)南大學(xué)》2017年碩士論文

【摘要】：隨著經(jīng)濟(jì)迅速發(fā)展和科學(xué)技術(shù)的不斷進(jìn)步,社會的各行業(yè)對板帶鋼的需求也日益增長,因此,對板帶鋼的質(zhì)量要求也越來越高。板形作為檢測板帶鋼質(zhì)量的重要指標(biāo),如何科學(xué)地解決板形的問題也早已成為國內(nèi)外專家研究的重要課題。本文以人工智能理論中神經(jīng)網(wǎng)絡(luò)作為研究基礎(chǔ),選擇冷軋板帶鋼的板形缺陷識別作為研究課題。本文通過對冷軋帶鋼板形缺陷模式的分析,提出了一種基于Elman神經(jīng)網(wǎng)絡(luò)的板形識別方法。首先,本文詳細(xì)分析了Elman神經(jīng)網(wǎng)絡(luò)的優(yōu)缺點,對于該神經(jīng)網(wǎng)絡(luò)訓(xùn)練速度慢、存在早熟現(xiàn)象的缺陷,提出利用遺傳算法對網(wǎng)絡(luò)進(jìn)行優(yōu)化改進(jìn)。本文進(jìn)而分析了遺傳算法的性能特點,發(fā)現(xiàn)遺傳算法求解多峰非線性問題時,容易陷入局部極值和出現(xiàn)早熟收斂現(xiàn)象。為此,本文將人工免疫思想、自適應(yīng)機(jī)制和混沌優(yōu)化思想引入算法的進(jìn)化過程,對種群個體進(jìn)行免疫選擇、自適應(yīng)交叉、混沌變異,從而增強(qiáng)算法的全局搜索能力,提高算法搜索精度,實現(xiàn)對遺傳算法的改進(jìn),進(jìn)而提出了混沌免疫遺傳優(yōu)化算法。然后,本文采用此算法對Elman神經(jīng)網(wǎng)絡(luò)的初始權(quán)值和閾值參數(shù)進(jìn)行優(yōu)化,建立了一種改進(jìn)Elman網(wǎng)絡(luò)的板形缺陷識別模型,并利用若干組測試樣本數(shù)據(jù)對該模型方法進(jìn)行仿真實驗。通過比較該模型和BP、Elman以及GA-Elman網(wǎng)絡(luò)的仿真識別效果,證明了用混沌免疫遺傳算法優(yōu)化的Elman網(wǎng)絡(luò)板形缺陷識別方法,解決了網(wǎng)絡(luò)收斂速度慢、易早熟的問題,相比單純采用遺傳算法優(yōu)化的網(wǎng)絡(luò)模型具有更高的識別精度,識別速度更快,因此,該方法也對板形進(jìn)行實時控制具有重大意義。
[Abstract]:With the rapid development of economy and the continuous progress of science and technology, the demand for strip steel in various sectors of society is also increasing day by day, therefore, the quality requirement of strip steel is becoming higher and higher. As an important index to measure the quality of plate and strip, how to solve the problem of shape scientifically has already become an important subject of experts at home and abroad. In this paper, the neural network in artificial intelligence theory is used as the research foundation, and the shape defect identification of cold rolled strip is selected as the research topic. In this paper, a shape recognition method based on Elman neural network is proposed by analyzing the shape defect pattern of cold rolled strip. Firstly, this paper analyzes the advantages and disadvantages of Elman neural network in detail. In view of its slow training speed and premature phenomenon, the genetic algorithm is used to optimize and improve the neural network. In this paper, the performance characteristics of genetic algorithm are analyzed. It is found that genetic algorithm is prone to fall into local extremum and premature convergence when solving multi-peak nonlinear problems. In this paper, the idea of artificial immune, adaptive mechanism and chaos optimization are introduced into the evolutionary process of the algorithm, and the immune selection, adaptive crossover and chaos mutation of the individual population are carried out, so as to enhance the global search ability of the algorithm. The search accuracy of the algorithm is improved and the genetic algorithm is improved. A chaotic immune genetic optimization algorithm is proposed. Then, this algorithm is used to optimize the initial weights and threshold parameters of Elman neural network, and a shape defect recognition model of improved Elman neural network is established, and some test samples are used to simulate the model. By comparing the simulation results of the model with that of BP Elman and GA-Elman neural networks, it is proved that the Elman network shape defect recognition method optimized by chaos immune genetic algorithm can solve the problem of slow convergence and premature convergence of the network. Compared with the network model optimized by genetic algorithm, this method has higher recognition accuracy and faster recognition speed. Therefore, this method also has great significance for real-time shape control.
【學(xué)位授予單位】：濟(jì)南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TG335;TP183

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本文編號：1793282