شبکه عصبی فازی مین- ماکس چندسطحی با باکس‌های وزن‌دار
الموضوعات :
رضا داوطلب
1
(دانشگاه تبریز)
محمدعلی بالافر
2
(دانشگاه تبریز)
محمدرضا فیضی درخشی
3
(دانشگاه تبریز)
الکلمات المفتاحية: طبقه‌بندی شبکه عصبی فازی مین- ماکس یادگیری ماشین وزن‌دار,
ملخص المقالة :
در این مقاله شبکه عصبی فازی مین- ماکس چندسطحی با باکس‌های وزن‌دار (WL-FMM) برای استفاده در کلاس‌بندی ارائه می‌گردد که یک ابزار یادگیری با نظارت بسیار سریع بوده و قادر به یادگیری داده‌ها به صورت برخط و تک‌گذار است. در این روش برای حل مشکل نواحی همپوشان که از مشکلات همیشگی روش‌های فازی مین- ماکس بوده، از باکس‌هایی با اندازه کوچک‌تر و وزن بیشتر استفاده می‌شود. این کار باعث افزایش دقت طبقه‌بندی شبکه در نواحی مرزی نمونه‌ها می‌گردد. همچنین با توجه به تغییراتی که در ساختار الگوریتم داده شده و بر اساس نتایج آزمایشی به دست آمده، روش ارائه‌شده نسبت به روش‌های مشابه از پیچیدگی زمانی و مکانی کمتری برخوردار بوده و نسبت به پارامترهایی که از طرف کاربر مشخص می‌شود، حساسیت کمتری دارد.
[1] L. A. Zadeh, "Fuzzy Sets," Inform. and Control, vol. 8, pp. 189-200, 1965.
[2] J. C. Bezdek, S. K. Pal, and IEEE Neural Networks Council., Fuzzy models for pattern recognition: methods that search for structures in data, New York: Institute of Electrical and Electronics Engineers, 1992.
[3] J. Vieira, F. M. Dias, and A. Mota, Neuro-Fuzzy Systems: A Survey, 2004.
[4] S. Mitra and Y. Hayashi, "Neuro-fuzzy rule generation: survey in soft computing framework," Neural Networks, IEEE Trans. on, vol. 11, no. 3, pp. 748-768, May 2000.
[5] D. Nauck and R. Kruse, What Are Neuro-Fuzzy Classifiers?, 1997.
[6] A. Bargiela, W. Pedrycz, and M. Tanaka, "An inclusion/exclusion fuzzy hyperbox classifier," International J. of Knowledge Based Intelligent Engineering Systems, vol. 8, no. 2, pp. 91-98, Aug. 2004.
[7] B. Gabrys and A. Bargiela, "General fuzzy min-max neural network for clustering and classification," IEEE Trans. on Neural Networks, vol. 11, no. 3, pp. 769-783, May 2000.
[8] A. V. Nandedkar and P. K. Biswas, "A fuzzy min-max neural network classifier with compensatory neuron architecture," IEEE Trans. on Neural Networks, vol. 18, no. 1, pp. 42-54, Jan. 2007.
[9] P. K. Simpson, "Fuzzy min-max neural networks I: classification," IEEE Trans. on Neural Networks, vol. 3, no. 5, pp. 776-786, Sep. 1992.
[10] A. Joshi, N. Ramakrishman, E. N. Houstis, and J. R. Rice, "On neurobiological, neuro-fuzzy, machine learning, and statistical pattern recognition techniques," IEEE Trans. on Neural Networks, vol. 8, no. 1, pp. 18-31, Jan. 1997.
[11] P. K. Simpson, "Fuzzy min-max neural networks, part 2, clustering," IEEE Trans. on Fuzzy Systems, vol. 1, no. 1, pp. 32-45, Feb. 1993.
[12] A. V. Nandedkar and P. K. Biswas, "A reflex fuzzy min max neural network for semi-supervised learning," J. of Intelligent Systems, vol. 17, no. 1, pp. 5-18, Aug. 2008.
[13] A. V. Nandedkar and P. K. Biswas, "A granular reflex fuzzy min-max neural network for classification," IEEE Trans. on Neural Networks, vol. 20, no. 7, pp. 1117-1134, Jul. 2009.
[14] H. Zhang, J. Liu, D. Ma, and Z. Wang, "Data-core-based fuzzy min-max neural network for pattern classification," IEEE Trans. on Neural Networks, vol. 22, no. 12, pp. 2339-2352, Nov. 2011.
[15] R. Davtalab, M. H. Dezfoulian, and M. Mansoorizadeh, "Multi-level fuzzy min-max neural network classifier," IEEE Trans. Neural Netw Learn Syst, vol. 25, no. 3, pp. 470-482, Mar. 2014.
[16] Q. Hua, "A new union-set learning algorithm in fuzzy min-max neural networks," Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence, vol. 14, no. 4, p. 413, 2001.
[17] B. Gabrys, "Agglomerative learning algorithms for general fuzzy min-max neural network," J. of VLSI Signal Processing Systems for Signal, Image, and Video Technology, vol. 32, no. 1, pp. 67-82, Aug. 2002.
[18] A. V. Nandedkar and P. K. Biswas, "A general fuzzy min max neural network with compensatory neuron architecture," in Proc. Int. Conf. on Knowledge-Based and Intelligent Information and Engineering Systems, pp. 1160-1167, 2005.
[19] P. F. Peng, L. J. Yang, and Q. G. Zhang, "Improvement and application based on general fuzzy min-max neural network," Wuhan Ligong Daxue Xuebao/J. of Wuhan University of Technology, vol. 26, no. 10, p. 87, Oct. 2004.
[20] P. F. Peng, L. J. Yang, and Q. G. Zhang, "Unsupervised general fuzzy min-max artificial neural network," Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, vol. 26, no. 10, pp. 1503-1505+1536, Oct. 2004.
[21] G. Tang, Y. Dai, and G. Liu, "Radar range profile classification based on fuzzy min-max neural networks," Binggong Xuebao/Acta Armamentarii, vol. 25, no. 2, pp. 218-221, Feb. 2004.
[22] H. J. Kim and H. S. Yang, "A weighted fuzzy min-max neural network and its application to feature analysis," in Proc. of the First Int. Conf. on Advances in Natural Computation, vol. 3, pp. 1178-1181, Changsha, China 27-29 Aug. 2005.
[23] C. C. Chen, "Design of a fuzzy min-max hyperbox classifier using a supervised learning method," Cybernetics and Systems, vol. 37, pp. 329-346, 2006.
[24] J. Hu, J. Yang, and J. Gao, "Ordination-fuzzy min-max neural network classifier on unlabelled pattern classification," Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence, vol. 20, no. 2, pp. 173-179, Feb. 2007.
[25] J. Yang, J. Gao, X. H. Xu, and X. Liu, "Hierarchical fuzzy min-max clustering algorithm," Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence, vol. 20, no. 4, pp. 558-564, Apr. 2007.
[26] A. Quteishat and C. P. Lim, "A modified fuzzy min-max neural network with rule extraction and its application to fault detection and classification," Applied Soft Computing J., vol. 8, no. 2, pp. 985-995, Mar. 2008.
[27] R. Zemouri, D. Racoceanu, N. Zerhouni, E. Minca, and F. Filip, "Training the recurrent neural network by the fuzzy min-max algorithm for fault prediction," in Proc. 2nd Mediterranean Conference on Intelligent Systems and Automation. CISA'09, pp. 85-90, Zarzis, Tunisia, 23-25 Mar. 2009.
[28] X. Ge and E. J. Ding, "Research on parameters of fuzzy min-max neural networks," Kongzhi yu Juece/Control and Decision, vol. 25, no. 8, pp. 295-298, Aug. 2010.
[29] A. Quteishat, C. P. Lim, and K. S. Tan, "A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification," IEEE Trans. on Systems, Man, and Cybernetics Part A: Systems and Humans, vol. 40, no. 3, pp. 641-650, May 2010.
[30] P. Rey-del-Castillo and J. Cardenosa, "Fuzzy min-max neural networks for categorical data: application to missing data imputation," Neural Computing and Applications, vol. 21, no. 6, pp. 1349-1362, Sep. 2012.
[31] R. Davtalab, M. Parchami, M. H. Dezfoulian, M. Mansourizade, and B. Akhtar, "M-FMCN: modified fuzzy min-max classifier using compensatory neurons," in the Proc. of the 11th WSEAS International Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases, Cambridge, UK, pp. 77-82, Feb. 2012.
[32] C. Blake, E. Keogh, and C. Merz, UCI Repository of Machine Learning Databases, Irvine: Department of Information and Computer Science, University of California, 1998.
