|
|
|
| An Improved Wang-Mendel Method Based on Cooperation Degree of Sample and Self-Organizing Mapping |
| GOU Jin,CHEN Wen-Yu |
| College of Computer Science Technology,Huaqiao University,Xiamen 361021 |
|
|
|
|
Abstract Wang-Mendel algorithm is commonly used as a classic method to generate fuzzy rule base. But rules with low confidence are usually extracted when noise appears in the sample data set,while its efficiency also often drops fast when the scale of sample data increases. To solve those problems,two methods,cooperation relationship and self-organizing mapping (SOM) neural network,are introduced. Cooperation relationship among sample data improves the accuracy of rules and approximation ability to the original model. On the other hand,SOM can well preprocess sample data for denoising and reduce its scale through a self-adaptive learning procedure of weights network. Then an improved Wang-Mendel algorithm is proposed based on cooperation relationship degree of sample data and SOM. The experimental results,including trigonometric function approximation and artificial driving simulation of a train operation control system,show its completeness,robustness and operating efficiency.
|
|
Received: 06 March 2013
|
|
|
|
|
|
[1] Wang Lixin, Mendel J M. Generating Fuzzy Rules by Learning from Examples. IEEE Trans on Systems, Man and Cybernetics, 1992, 22(6): 1414-1427
[2] Wang Lixin. The WM Method Completed: A Flexible Fuzzy System Approach to Data Mining. IEEE Trans on Fuzzy Systems, 2003, 11(6): 768-782
[3] Wang Yongfu, Chai Tianyou. Mining Fuzzy Rules from Data and Its System Implementation. Journal of Systems Engineering, 2005, 20(5): 497-503 (in Chinese)
(王永富,柴天佑.从数据中挖掘模糊规则及其系统实现.系统工程学报, 2005, 20(5): 497-503)
[4] Wang Yongfu, Wang Dianhui, Chai Tianyou. Extraction of Fuzzy Rules with Completeness and Robustness. Acta Automatica Sinica, 2010, 36(9): 1337-1342 (in Chinese)
(王永富,王殿辉,柴天佑. 一个具有完备性和鲁棒性的模糊规则提取算法.自动化学报, 2010, 36(9): 1337-1342)
[5] Zhang Li, Gao Xianwen, Li Shenming, et al. Constrained Fuzzy Predictive Control Based on Wang-Mendel Model. Control and Decision, 2010, 25(9): 1384-1388 (in Chinese)
(张 立,高宪文,李申明,等.基于Wang-Mendel模型的有约束模糊预测控制.控制与决策, 2010, 25(9): 1384-1388)
[6] Yu Dongjun, Chen Yihua, Yu Haiying. Fuzzy Rule Extraction by Using SOM and Wang-Mendel Method. Journal of Nanjing University
of Science and Technology, 2011, 35(6): 759-763 (in Chinese)
(於东军,谌贻华,于海瑛.融合自组织映射与Wang-Mendel方法的模糊规则提取.南京理工大学学报, 2011, 35(6): 759-763)
[7] Chen Chunhao, Hong T P, Tseng V S. Fuzzy Data Mining for Time-Series Data. Applied Soft Computing, 2012, 12(1): 536-542
[8] Yang Xueming, Yuan Jiangye, Yuan Jinsha, et al. An Improved WM Method Based on PSO for Electric Load Forecasting. Expert Systems with Applications, 2010, 37(12): 8036-8041
[9] Shi Xiaomei, Mei Hongyan, Zhu Tianhua, et al. A New Fuzzy Rule Extraction Method. Journal of Liaoning University of Technology:Natural Science Edition, 2012, 32(1): 22-26 (in Chinese)
(史晓梅,梅红岩,朱田华,等. 一种新的模糊规则提取方法.辽宁工业大学学报:自然科学版, 2012, 32(1): 22-26)
[10] Casillas J, Cordon O, Herrera F. Improving the Wang and Mendel′s Fuzzy Rule Learning Method by Inducing Cooperation among Rules // Proc of the 8th Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems. Madrid, Spain, 2000: 1682-1688
[11] Chen Wenyu, Gou Jin. Improved Wang-Mendel Scheme Based on Cooperation among Input Variables. International Review on Computers and Software, 2012, 7(5): 2685-2689
[12] Kohonen T. Physiological Interpretation of the Self-Organizing Map Algorithm. Neural Networks, 1993, 6(7): 895-905
[13] Nasr M B, Chtourou M. A Self-Organizing Map-Based Initialization for Hybrid Training of Feed Forward Neural Networks. Applied Soft Computing, 2011, 11(8): 4458-4464
[14] Kohonen T. Essentials of the Self-Organizing Map. Neural Networks, 2013, 37(1): 52-65
[15] Kita E, Kan S, Fei Zhen. Investigation of Self-Organizing Map for Genetic Algorithm. Advances in Engineering Software, 2010, 41(2): 148-153
[16] Astudillo C A, Oommen B J. Imposing Tree-Based Topologies onto Self-Organizing Maps. Information Sciences, 2011, 181(18): 3798-3815
[17] Chang P C, Liao T W. Combining SOM and Fuzzy Rule Base for Flow Time Prediction in Semiconductor Manufacturing Factory. Applied Soft Computing, 2006, 6(2): 198-206
[18] Wu Dongrui, Mendel J N. Linguistic Summarization Using IF-THEN Rules and Interval Type-2 Fuzzy Sets. IEEE Trans on Fuzzy Systems, 2011, 19(1): 136-151
[19] Zhang Liquan, Shao Cheng. An Adaptive Learning Method for the Generation of Fuzzy Inference System from Data. Acta Automatica Sinica, 2008, 34(1): 80-84 (in Chinese)
(张立权,邵 诚.利用数据产生模糊推理系统的自适应学习方法.自动化学报, 2008, 34(1): 80-84)
[20] Tang Min, Chen Xia, Hu Weidong, et al. Generation of a Probabilistic Fuzzy Rule Base by Learning from Examples. Information Sciences, 2012, 217: 21-30
[21] Meng D, Pei Z. Extracting Linguistic Rules from Data Sets Using Fuzzy Logic and Genetic Algorithms. Neurocomputing, 2012, 78(1): 48-54 |
|
|
|
2013, Vol. 26 
