本文關(guān)鍵詞： 模糊規(guī)則提取 WM算法 支持向量機(jī) 最大相對(duì)誤差最小　出處：《華僑大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：混凝土強(qiáng)度、流和坍落度是建筑工程和土木工程中混凝土質(zhì)量控制的研究重點(diǎn)。由于不同地區(qū)溫度、濕度等外界因素干擾,以及混凝土中各組分間復(fù)雜的物理、化學(xué)反應(yīng),使得混凝土組分反預(yù)測(cè)更加復(fù)雜。因此,解決混凝土組分反預(yù)測(cè)有著重要意義。模糊規(guī)則可以通過模擬人的思考方式,將專家知識(shí)轉(zhuǎn)化成模糊規(guī)則的形式,可以得到不錯(cuò)的效果,然而模糊規(guī)則提取的好壞決定了模糊系統(tǒng)的預(yù)測(cè)能力。目前常用的模糊規(guī)則提取算法是Wang-Mendel算法(WM算法),該算法是由Wang和Mendel提出的,可以較好解決實(shí)際工程應(yīng)用中存在的非線性、高維和時(shí)變性問題。但是WM算法在性能上,如預(yù)測(cè)精度、運(yùn)行效率、魯棒性和完備性還有改進(jìn)空間。另外,支持向量機(jī)(Support Vector Machine,SVM)對(duì)于非線性、小樣本等問題有著不錯(cuò)的效果,但是還有改進(jìn)的空間。因此,為了解決混凝土組分反預(yù)測(cè)問題,本文展開了以下研究:(1)基于聚類算法改進(jìn)WM算法的預(yù)測(cè)精度、運(yùn)行效率、魯棒性和完備性。本文引入快速搜索和密度峰發(fā)現(xiàn)聚類算法(Clustering by Fast Search and Find of Density Peaks,FSFDP)對(duì)數(shù)據(jù)進(jìn)行預(yù)處理,去除噪聲數(shù)據(jù),從而改進(jìn)算法的魯棒性和預(yù)測(cè)精度。通過使用樣本之間的關(guān)系信息,可以對(duì)模糊規(guī)則庫中缺失的規(guī)則進(jìn)行預(yù)測(cè),從而保證算法的完備性。另外,在數(shù)據(jù)規(guī)模較大、數(shù)據(jù)屬性和模糊區(qū)間個(gè)數(shù)較多的時(shí)候,使用FSFDP算法中的聚類中心點(diǎn)提取模糊規(guī)則,可以大大減少模糊規(guī)則數(shù)量,從而提高算法的效率。并使用實(shí)驗(yàn)來驗(yàn)證算法的性能。(2)基于粒子群優(yōu)化算法(Particle Swarm Optimization,PSO)的最大相對(duì)誤差最小SVM算法。使用相對(duì)誤差最小改進(jìn)SVM的最大間隔約束條件,使得改進(jìn)的算法更符合實(shí)際工程中的應(yīng)用;使用PSO最小化最大相對(duì)誤差對(duì)改進(jìn)的SVM進(jìn)行參數(shù)優(yōu)化,得到性能更佳的模型。并使用混凝土坍落度試驗(yàn)數(shù)據(jù)集驗(yàn)證算法的可行性。(3)基于相對(duì)誤差支持向量機(jī)改進(jìn)的WM算法。由于WM算法的模糊規(guī)則后件使用的是集合形式,為了增強(qiáng)模糊規(guī)則的擬合能力,本文使用改進(jìn)的支持向量機(jī)作為模糊規(guī)則的后件,從而提高算法的性能。最后,使用混凝土強(qiáng)度數(shù)據(jù)集對(duì)算法進(jìn)行驗(yàn)證。
[Abstract]:The strength, flow and slump of concrete are the key points of concrete quality control in construction and civil engineering. Because of the interference of temperature, humidity and other external factors in different areas, and the complex physics of each component in concrete. Chemical reaction makes the back prediction of concrete component more complicated. Therefore, it is very important to solve the inverse prediction of concrete component. Fuzzy rules can be used to simulate human thinking. The expert knowledge can be transformed into the form of fuzzy rules, and good results can be obtained. However, the quality of fuzzy rule extraction determines the prediction ability of fuzzy system. At present, the commonly used fuzzy rule extraction algorithm is Wang-Mendel algorithm. The proposed algorithm is proposed by Wang and Mendel, which can solve the nonlinear, high dimensional and time-varying problems in practical engineering applications, but the WM algorithm has good performance, such as prediction accuracy. There is also room for improvement in efficiency, robustness and completeness. In addition, support vector machine support Vector machine is nonlinear. Small samples and other problems have good results, but there is room for improvement. Therefore, in order to solve the problem of back prediction of concrete composition. In this paper, the following research is carried out: (1) improve the prediction accuracy and running efficiency of WM algorithm based on clustering algorithm. Robustness and completeness. This paper introduces a fast search and density peak discovery clustering algorithm (. Clustering by Fast Search and Find of Density Peaks. FSFDP is used to preprocess the data to remove the noise data, so as to improve the robustness and prediction accuracy of the algorithm. The missing rules in the fuzzy rule base can be predicted to ensure the completeness of the algorithm. In addition, when the data scale is large, the number of data attributes and fuzzy intervals is large. The number of fuzzy rules can be greatly reduced by extracting fuzzy rules by using clustering center points in FSFDP algorithm. In order to improve the efficiency of the algorithm, and use experiments to verify the performance of the algorithm. 2) Particle Swarm Optimization based on particle swarm optimization algorithm. The maximum relative error minimum (SVM) algorithm is used to improve the maximum interval constraint condition of SVM, which makes the improved algorithm more suitable for practical engineering applications. PSO is used to minimize the maximum relative error to optimize the parameters of the improved SVM. Get better performance models. Use concrete slump test data set to verify the feasibility of the algorithm. The improved WM algorithm based on relative error support vector machine. In order to enhance the fitting ability of fuzzy rules, the improved support vector machine (SVM) is used to improve the performance of the algorithm. Finally, the concrete strength data set is used to verify the algorithm.
【學(xué)位授予單位】：華僑大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP18;TU528

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 桂衛(wèi)華;陳曉方;陽春華;謝永芳;;知識(shí)自動(dòng)化及工業(yè)應(yīng)用[J];中國(guó)科學(xué):信息科學(xué);2016年08期

2 閆春嶺;張愛玲;;再生混凝土坍落度自適應(yīng)模糊推理模型[J];混凝土;2014年08期

3 緱錦;陳文瑜;;基于樣本相關(guān)度和SOM的改進(jìn)型Wang-Mendel算法[J];模式識(shí)別與人工智能;2013年11期

4 張研;蘇國(guó)韶;燕柳斌;;高強(qiáng)混凝土強(qiáng)度預(yù)測(cè)的高斯過程機(jī)器學(xué)習(xí)模型[J];混凝土;2011年11期

5 何曉鳳;;基于PSO-BP神經(jīng)網(wǎng)絡(luò)的混凝土抗壓強(qiáng)度預(yù)測(cè)[J];微型機(jī)與應(yīng)用;2011年20期

6 何曉鳳;周紅標(biāo);;基于GA-BP的混凝土抗壓強(qiáng)度預(yù)測(cè)研究[J];淮陰工學(xué)院學(xué)報(bào);2011年03期

7 汪廷華;陳峻婷;;核函數(shù)的度量研究進(jìn)展[J];計(jì)算機(jī)應(yīng)用研究;2011年01期

8 張立;高憲文;李申明;趙娟平;王介生;;基于Wang-Mendel模型的有約束模糊預(yù)測(cè)控制[J];控制與決策;2010年09期

9 王永富;王殿輝;柴天佑;;一個(gè)具有完備性和魯棒性的模糊規(guī)則提取算法[J];自動(dòng)化學(xué)報(bào);2010年09期

10 吳德會(huì);;基于LS-SVM的粉煤灰混凝土強(qiáng)度預(yù)測(cè)模型[J];新型建筑材料;2007年03期

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