基于WM與PSO的模糊分類器優(yōu)化研究
發(fā)布時間：2018-10-18 08:43
【摘要】：從樣本中提取規(guī)則進而進行構建模糊分類器是一種有效的建模方式。Wang-Mendel(WM)方法根據(jù)模糊數(shù)學理論方法從數(shù)據(jù)中直接提取模糊規(guī)則。WM方法具有簡單、高效實用的特點。但是,處理過程中,該算法易提取出低效的模糊規(guī)則。因此對于WM方法提取后的模糊規(guī)則庫需要進一步的優(yōu)化整合處理。粒子群優(yōu)化算法(Particle Swarm Optimization,PSO)是一種基于迭代的進化計算方法。PSO算法在模糊分類器領域的應用主要是對模糊知識庫進行融合,從而將原有模糊規(guī)則庫的結構整合成另一種擁有最好執(zhí)行效率的組合。但是算法收斂速度過慢。本文采用基于PSO算法的優(yōu)化算法智能單粒子優(yōu)化算法(intelligence single particle optimizer,ISPO)來對規(guī)則庫進行優(yōu)化處理,ISPO算法相比PSO算法具有較快的算法收斂效果。通過對WM算法分析發(fā)現(xiàn),雖然算法可以實現(xiàn)高效的規(guī)則提取,但是由于沖突機制中的設置使得規(guī)則缺少樣本關聯(lián)度,從而導致規(guī)則庫的分類精度受到影響。為避免這一現(xiàn)象造成的影響,采用ISPO算法對規(guī)則庫進行進一步的優(yōu)化,在適應度函數(shù)中利用樣本關聯(lián)度對規(guī)則進行進一步的修改。從而加快算法的收斂速度。而且適應度函數(shù)與分類精度呈正相關關系,保證了規(guī)則庫的高準確率。反向進行思考,ISPO算法有一大缺陷就是算法初始的種群為隨機生成,這樣降低算法的收斂速度,而本方法初始化的粒子是具有一定準確度的規(guī)則庫,進而加快了尋優(yōu)的速度。根據(jù)以上分析,本文提出一種基于WM與ISPO的模糊分類器WPFS算法對WM算法和ISPO算法更進一步分析發(fā)現(xiàn),兩種算法都擁有較高并行能力,因此,將算法進行并行化重構,設計出基于WM與ISPO的并行模糊分類器P-WPFS算法。為驗證并行分類器模型有效性,將并行模型與MapReduce模型結合形成WPFS-MR模糊分類器模型,應用于大規(guī)模數(shù)據(jù)集分類問題。WPFS-MR模糊分類器模型較大提高了算法的處理效率,使得算法在可接受時間范圍內給出分類結果。并且同時可以保證分類結果的準確度保持較高的精度。解決了面對大數(shù)據(jù)分類難題,模糊分類器效率低下的問題。
[Abstract]:Extracting rules from samples and constructing fuzzy classifier is an effective modeling method. Wang-Mendel (WM) method extracts fuzzy rules directly from data according to fuzzy mathematics theory. WM method is simple, efficient and practical. However, in the process of processing, the algorithm is easy to extract inefficient fuzzy rules. Therefore, the fuzzy rule base extracted by WM method needs further optimization and integration. Particle Swarm Optimization (Particle Swarm Optimization,PSO) is an iterative evolutionary algorithm. The application of PSO algorithm in fuzzy classifier is to fuse the fuzzy knowledge base. Thus, the structure of the original fuzzy rule base is integrated into another combination with the best execution efficiency. But the convergence rate of the algorithm is too slow. In this paper, the intelligent single particle optimization algorithm (intelligence single particle optimizer,ISPO) based on PSO algorithm is used to optimize the rule base. Compared with PSO algorithm, ISPO algorithm has faster convergence effect. Through the analysis of the WM algorithm, it is found that although the algorithm can achieve efficient rule extraction, the rules lack of sample correlation because of the conflict mechanism, which results in the impact of the classification accuracy of the rule base. In order to avoid the influence caused by this phenomenon, the rule base is further optimized by ISPO algorithm, and the rule is further modified by sample correlation degree in fitness function. In order to speed up the convergence of the algorithm. Furthermore, the fitness function is positively related to the classification accuracy, which ensures the high accuracy of the rule base. On the contrary, the ISPO algorithm has a big defect that the initial population of the algorithm is randomly generated, which reduces the convergence speed of the algorithm, while the particle initialized by this method is a rule base with certain accuracy, thus speeding up the speed of optimization. Based on the above analysis, a fuzzy classifier WPFS algorithm based on WM and ISPO is proposed to further analyze the WM algorithm and ISPO algorithm. It is found that both algorithms have high parallelism ability, so the algorithm is parallelized and reconstructed. A parallel fuzzy classifier P-WPFS algorithm based on WM and ISPO is designed. In order to verify the validity of the parallel classifier model, the parallel model and MapReduce model are combined to form the WPFS-MR fuzzy classifier model, which is applied to the large-scale data set classification problem. The WPFS-MR fuzzy classifier model greatly improves the processing efficiency of the algorithm. The classification results are given in the acceptable time range. At the same time, the accuracy of the classification results can be guaranteed to maintain a high accuracy. It solves the problem of low efficiency of fuzzy classifier in the face of big data classification problem.
【學位授予單位】：華僑大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP18
本文編號：2278599
[Abstract]:Extracting rules from samples and constructing fuzzy classifier is an effective modeling method. Wang-Mendel (WM) method extracts fuzzy rules directly from data according to fuzzy mathematics theory. WM method is simple, efficient and practical. However, in the process of processing, the algorithm is easy to extract inefficient fuzzy rules. Therefore, the fuzzy rule base extracted by WM method needs further optimization and integration. Particle Swarm Optimization (Particle Swarm Optimization,PSO) is an iterative evolutionary algorithm. The application of PSO algorithm in fuzzy classifier is to fuse the fuzzy knowledge base. Thus, the structure of the original fuzzy rule base is integrated into another combination with the best execution efficiency. But the convergence rate of the algorithm is too slow. In this paper, the intelligent single particle optimization algorithm (intelligence single particle optimizer,ISPO) based on PSO algorithm is used to optimize the rule base. Compared with PSO algorithm, ISPO algorithm has faster convergence effect. Through the analysis of the WM algorithm, it is found that although the algorithm can achieve efficient rule extraction, the rules lack of sample correlation because of the conflict mechanism, which results in the impact of the classification accuracy of the rule base. In order to avoid the influence caused by this phenomenon, the rule base is further optimized by ISPO algorithm, and the rule is further modified by sample correlation degree in fitness function. In order to speed up the convergence of the algorithm. Furthermore, the fitness function is positively related to the classification accuracy, which ensures the high accuracy of the rule base. On the contrary, the ISPO algorithm has a big defect that the initial population of the algorithm is randomly generated, which reduces the convergence speed of the algorithm, while the particle initialized by this method is a rule base with certain accuracy, thus speeding up the speed of optimization. Based on the above analysis, a fuzzy classifier WPFS algorithm based on WM and ISPO is proposed to further analyze the WM algorithm and ISPO algorithm. It is found that both algorithms have high parallelism ability, so the algorithm is parallelized and reconstructed. A parallel fuzzy classifier P-WPFS algorithm based on WM and ISPO is designed. In order to verify the validity of the parallel classifier model, the parallel model and MapReduce model are combined to form the WPFS-MR fuzzy classifier model, which is applied to the large-scale data set classification problem. The WPFS-MR fuzzy classifier model greatly improves the processing efficiency of the algorithm. The classification results are given in the acceptable time range. At the same time, the accuracy of the classification results can be guaranteed to maintain a high accuracy. It solves the problem of low efficiency of fuzzy classifier in the face of big data classification problem.
【學位授予單位】：華僑大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP18
【參考文獻】
相關期刊論文 前10條
1 陳剛;;科學研究大數(shù)據(jù)挑戰(zhàn)[J];科學通報;2015年Z1期
2 緱錦;陳文瑜;;基于樣本相關度和SOM的改進型Wang-Mendel算法[J];模式識別與人工智能;2013年11期
3 孟小峰;慈祥;;大數(shù)據(jù)管理:概念、技術與挑戰(zhàn)[J];計算機研究與發(fā)展;2013年01期
4 李國杰;程學旗;;大數(shù)據(jù)研究:未來科技及經濟社會發(fā)展的重大戰(zhàn)略領域——大數(shù)據(jù)的研究現(xiàn)狀與科學思考[J];中國科學院院刊;2012年06期
5 陳如明;;大數(shù)據(jù)時代的挑戰(zhàn)、價值與應對策略[J];移動通信;2012年17期
6 李安華;尹紅艷;周衛(wèi)紅;;基于遺傳算法的Mamdani模糊分類器的研究[J];云南民族大學學報(自然科學版);2010年03期
7 張洪祥;毛志忠;;基于時間序列的模糊聚類與規(guī)則提取信用評價模型[J];東北大學學報(自然科學版);2010年04期
8 紀震;周家銳;廖惠連;吳青華;;智能單粒子優(yōu)化算法[J];計算機學報;2010年03期
9 職為梅;王芳;范明;楊勇;;并行環(huán)境下的同步異步PSO算法[J];計算機技術與發(fā)展;2009年03期
10 陳鐵明;龔榮盛;SAMUEL H Huang;;一種新的快速模糊規(guī)則提取方法[J];控制與決策;2008年09期
相關碩士學位論文 前2條
1 周宗偉;高性能大規(guī)模模式匹配算法研究[D];清華大學;2008年
2 蘭利寶;基于視頻的多運動目標識別技術研究[D];沈陽理工大學;2008年
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