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姓名 杜家豪(Chia-Hao Tu)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱 智慧型模糊類神經計算使用非對稱模糊類神經網路系統與球型複數模糊集
(Intelligent Neuro-Fuzzy Computing with an Asymmetric Neuro-Fuzzy System and Sphere Complex Fuzzy Sets)
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摘要(中) 模糊類神經系統(Neuro-fuzzy system, NFS)結合了人工神經網絡(Artificial neural network, ANN)的學習能力和模糊推論系統(Fuzzy inference system, FIS)明確表達知識的能力。然而,傳統模糊類神經系統存在兩個問題影響了系統運作的效率,這兩個問題分別為模型的對稱結構和多輸入單輸出架構。本篇論文提出非對稱式模糊類神經系統(Asymmetric NFS, ANFS),此系統包含兩種機制解決上述存在於傳統模糊類神經系統的問題。首先,非對稱式模糊類神經系統中加入了非對稱層(Asymmetric layer)的結構,使得模型前鑑部層(Premise layer)與後鑑部層(Consequent layer)可以擁有不同的神經元數量。其次,模型使用球型複數模糊集(Sphere complex fuzzy sets, SCFSs)取代位於前鑑部層的傳統模糊集,使模型可以依據不同應用彈性調整輸出數量。此外,為了解決多目標預測模型所衍生的跨目標特徵選擇問題,論文中提出以影響資訊為基礎的跨目標特徵選擇演算法。而因應模型前鑑部層參數為非線性、後鑑部層參數為線性的特性,也提出了一個結合以狀態為基礎的鯨群優化演算法(State-based whale optimization algorithm, SWOA)和遞迴式最小平方估計法(Recursive least-square estimator, RLSE)的混合型學習演算法來優化模型參數。
論文設計三個實驗來驗證所提出方法的效能,包含雙函數近似、雙匯率預測、以及四個股票指數的預測。實驗結果顯示,所提出的方法可以同時對多個目標進行預測,且預測效能優於傳統模糊類神經系統與多數文獻中的方法。
摘要(英) The neuro-fuzzy system (NFS) is designed to exploit the learning abilities of an artificial neural network (ANN) and the explicit knowledge of a fuzzy inference system (FIS). However, the two problems of symmetric structure and the multiple-input single-output architecture in traditional NFSs affect system efficiency. An asymmetric NFS (ANFS) has been proposed in this dissertation to address the problems mentioned above with two mechanisms. Firstly, an asymmetric layer is added to the ANFS model, making the model has different neuron numbers in the premise and consequent layers. Secondly, the introduced sphere complex fuzzy sets (SCFSs) replace the traditional fuzzy sets, making the model output numbers adjustable for different applications. For resolving the cross-target feature selection problem existing in the multitarget prediction model, we proposed a feature selection algorithm based on influence information. Besides, a hybrid learning algorithm combining a state-based whale optimization algorithm with the recursive least-square estimator has been proposed to optimize the model.
Three experiments are designed to evaluate the proposed approach’s performance, including the dual function approximation, two exchange rate, and four stock index predictions. The experimental results indicate that the proposed approach can predict multiple targets simultaneously, having a favorable performance better than conventional NFS and other methods in the literature.
關鍵字(中) ★ 模糊類神經系統
★ 非對稱式模糊類神經系統
★ 箭靶法為基礎的非對稱模糊類神經系統
★ 快速箭靶法為基礎的非對稱模糊類神經系統
★ 球型複數模糊集
★ 以狀態為基礎的鯨群優化演算法
關鍵字(英) ★ Neuro-fuzzy system (NFS)
★ Asymmetric neuro-fuzzy system (ANFS)
★ Aim-object-based ANFS (AANFS)
★ Fast aim-object-based ANFS (FAANFS)
★ Sphere complex fuzzy set (SCFS)
★ State-based whale optimization algorithm (SWOA)
論文目次 中文摘要 i
Abstract ii
Table of Contents iii
List of Figures v
List of Tables vi
List of Symbols viii
List of Abbreviations xiii
Chapter I. Introduction 1
1.1 Research background and motivation 1
1.2 Research objectives 3
1.3 Organization of the dissertation 5
Chapter II. Related Work 6
2.1 Feature selection 6
2.2 Fuzzy sets 7
2.3 NFS 9
2.4 Learning algorithms 11
Chapter III. Methodology 13
3.1 Cross-target feature selection 13
3.1.1 Influence information matrix 13
3.1.2 Cross-target feature selection algorithm 16
3.2 Multiple-output neurons in the fuzzy-sets layer 18
3.3 Asymmetric layer 21
3.3.1 Aim-object method 21
3.3.2 Fast aim-object method 25
3.4 Asymmetric neuro-fuzzy system 26
3.5 Parameter learning 30
Chapter IV. Experimentation 37
4.1. Function approximation (dual functions) 38
4.2. Exchange prediction (forecasting of dual targets) 44
4.3. Stock index prediction (forecasting of four targets) 49
Chapter V. Discussions 57
5.1. Discussion of the cross-target feature selection 57
5.2. Discussion of the SWOA-RLSE hybrid learning algorithm 59
5.3. Discussion of the SMONFS and the ANFS-based models 60
5.4. Discussion of the AANFS and FAANFS 63
5.5. Discussion of the sphere complex fuzzy set and the proposed approaches 64
Chapter VI. Conclusions and Future Work 68
Appendix A. Derivation of the SCFS Mapping Values 71
A.1. Three-dimensional SCFS 71
A.2. Four-dimensional SCFS 71
References 73
參考文獻 [1] Hancock, M., “Artificial intelligence: opportunities and implications for the future of decision making.” Government Office for Science, 2016.
[2] Mitchell, T. M., Machine learning. McGraw Hill, 1997.
[3] Ransbotham, S., Gerbert, P., Reeves, M., Kiron, D. and Spira, M., “Artificial intelligence in business gets real.” MIT Sloan Management Review and Boston Consulting Group, Sep 2018.
[4] Brynjolfsson, E., Rock, D. and Syverson, C., Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. No. w24001, National Bureau of Economic Research, 2017.
[5] Anderson, J. A., An introduction to neural networks. MIT press, 1995.
[6] Faraway, J. and Chatfield, C., “Time series forecasting with neural networks: a comparative study using the air line data.” Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol. 47, No. 2, 1998, pp. 231-250.
[7] Jang, J. S., “ANFIS: adaptive-network-based fuzzy inference system.” IEEE transactions on systems, man, and cybernetics, Vol. 23, No. 3, 1993, pp. 665-685.
[8] Mitra, S., Datta, S., Perkins, T. and Michailidis, G., Introduction to machine learning and bioinformatics. Chapman & Hall/CRC, 2008.
[9] Paiva, R. P. and Dourado, A., “Interpretability and learning in neuro-fuzzy systems.” Fuzzy sets and systems, Vol. 147, No. 1, 2004, pp. 17-38.
[10] Shihabudheen, K. V. and Pillai, G. N., “Recent advances in neuro-fuzzy system: A survey.” Knowledge-Based Systems, Vol. 152, 2018, pp. 136-162.
[11] May, R., Dandy, G. and Maier, H., “Review of input variable selection methods for artificial neural networks.” Artificial neural networks-methodological advances and biomedical applications, 2011, pp. 19-44.
[12] Tu, C. H., Li, C., “Multitarget prediction—A new approach using sphere complex fuzzy sets.” Engineering Applications of Artificial Intelligence, Vol. 79, 2019, pp. 45-57.
[13] Ramot, D., Milo, R., Friedman, M. and Kandel, A., “Complex fuzzy sets.” IEEE Transactions on Fuzzy Systems, Vol. 10, No. 2, 2002, pp. 171–186.
[14] Li, C. and Chiang, T. W., “Complex fuzzy computing to time series prediction a multi-swarm PSO learning approach.” In Asian Conference on Intelligent Information and Database Systems, Vol. 6592, 2011, pp. 242–251.
[15] Li, C. and Chiang, T. W., “Function approximation with complex neuro-fuzzy system using complex fuzzy sets–a new approach.” New Generation Computing, Vol. 29, No. 3, 2011, pp. 261–276.
[16] Li, C. and Chiang, T. W., “Complex neurofuzzy ARIMA forecasting—a new approach using complex fuzzy sets.” IEEE Transactions on Fuzzy Systems, Vol. 21, No. 3, 2013, pp. 567–584.
[17] Cover, T. M. and Thomas, J. A., Elements of information theory. John Wiley & Sons, New York, NY, 1991.
[18] Shannon, C. E., “A mathematical theory of communication.” Bell System Technical Journal, Vol. 27, No. 3, 1948, pp. 379-423.
[19] Forman, G., “An extensive empirical study of feature selection metrics for text classification.” Journal of Machine Learning Research, Vol. 3, 2003, pp. 1289–1305.
[20] Naghibi, T., Hoffmann, S. and Pfister, B., “A semidefinite programming based search strategy for feature selection with mutual information measure.” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 37, No. 8, 2015, pp. 1529–1541.
[21] Sharmin, S., Shoyaib, M., Ali, A. A., Khan, M. A. H. and Chae, O., “Simultaneous feature selection and discretization based on mutual information.” Pattern Recognition, Vol. 91, 2019, pp. 162-174.
[22] Mirjalili, S. and Lewis, A., “The whale optimization algorithm.” Advances in Engineering Software, Vol. 95, 2016, pp. 51-67.
[23] Aljarah, I., Faris, H. and Mirjalili, S., “Optimizing connection weights in neural networks using the whale optimization algorithm.” Soft Computing, Vol. 22, No. 1, 2018, pp. 1-15.
[24] Bozorgi, S. M. and Yazdani, S., “IWOA: An Improved whale optimization algorithm for optimization problems.” Journal of Computational Design and Engineering, 2019.
[25] Jain, L., Katarya, R. and Sachdeva, S., “Opinion leader detection using whale optimization algorithm in online social network.” Expert Systems with Applications, Vol. 142, 2020, p. 113016.
[26] Frazzoli, E. and Dahleh, M., 6.241J Dynamic Systems and Control. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA, Spring 2011.
[27] John, G. H., Kohavi, R. and Pfleger, K., “Irrelevant features and the subset selection problem.” In Machine Learning: Proceedings of the Eleventh International Conference, 1994, pp. 121–129.
[28] Guyon, I. and Elisseeff, A., “An introduction to variable and feature selection.” Journal of Machine Learning Research, Vol. 3, 2003, pp. 1157–1182.
[29] Loughrey, J. and Cunningham, P., “Overfitting in wrapper-based feature subset selection: The harder you try the worse it gets.” Research and Development in Intelligent Systems XXI, 2005, pp. 33–43.
[30] Dash, M. and Liu, H., “Feature selection for classification.” Intelligent Data Analysis, Vol. 1, No. 3, 1997, pp. 131-156.
[31] Mariello, A. and Battiti, R., “Feature selection based on the neighborhood entropy.” IEEE transactions on neural networks and learning systems, Vol. 29, No. 12, 2018, pp. 6313-6322.
[32] Peng, H., Long, F. and Ding, C., “Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy.” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 8, 2005, pp. 1226–1238.
[33] Buckley, J. J., “Fuzzy complex numbers.” Fuzzy Sets and Systems, Vol. 33, No. 3, 1989, pp. 333–345.
[34] Tamir, D. E., Rishe, N. D. and Kandel, A., “Complex fuzzy sets and complex fuzzy logic an overview of theory and applications.” In Fifty Years of Fuzzy Logic and Its Applications. Studies in Fuzzyness and Soft Computing, Springer International Publishing, 2015, pp. 661–681.
[35] Yazdanbakhsh, O. and Dick, S., “A systematic review of complex fuzzy sets and logic.” Fuzzy Sets and Systems, Vol. 338, 2018, pp. 1–22.
[36] Wang, L. X., “A new look at type-2 fuzzy sets and type-2 fuzzy logic systems.” IEEE Transactions on Fuzzy Systems, Vol. 25, No. 3, 2017, pp. 693-706.
[37] Akram, M., Kavikumar, J. and Khamis, A. B., “Intuitionistic N-fuzzy set and its application in biΓ-ternary semigroups.” Journal of Intelligent & Fuzzy Systems, Vol. 30, No. 2, 2016, pp. 951-960.
[38] Ali, M. and Smarandache, F., “Complex neutrosophic set.” Neural Computing and Applications, Vol. 28, No. 7, 2017, pp. 1817-1834.
[39] Ali, M., Dat, L. Q. and Smarandache, F., “Interval complex neutrosophic set: formulation and applications in decision-making.” International Journal of Fuzzy Systems, Vol. 20, No. 3, 2018, pp. 986-999.
[40] Hao, Z., Xu, Z., Zhao, H. and Su, Z., “Probabilistic dual hesitant fuzzy set and its application in risk evaluation.” Knowledge-Based Systems, Vol. 127, 2017, pp. 16-28.
[41] Song, C., Zhao, H., Xu, Z. and Hao, Z., “Interval‐valued probabilistic hesitant fuzzy set and its application in the Arctic geopolitical risk evaluation.” International Journal of Intelligent Systems, Vol. 34, No. 4, 2019, pp.627-651.
[42] Zhai, J., Zhang, S. and Zhang, Y., “An extension of rough fuzzy set.” Journal of Intelligent & Fuzzy Systems, Vol. 30, No. 6, 2016, pp. 3311-3320.
[43] Garg, H., “Linguistic Pythagorean fuzzy sets and its applications in multiattribute decision‐making process.” International Journal of Intelligent Systems, Vol. 33, No. 6, 2018, pp. 1234-1263.
[44] Khan, M. S. A., Abdullah, S., Ali, A., Siddiqui, N. and Amin, F., “Pythagorean hesitant fuzzy sets and their application to group decision making with incomplete weight information.” Journal of Intelligent & Fuzzy Systems, Vol. 33, No. 6, 2017, pp. 3971-3985.
[45] Selvachandran, G., Maji, P. K., Abed, I. E. and Salleh, A. R., “Complex vague soft sets and its distance measures.” Journal of Intelligent & Fuzzy Systems, Vol. 31, No. 1, 2016, pp. 55-68.
[46] Zhang, H., Xiong, L. and Ma, W., “Generalized intuitionistic fuzzy soft rough set and its application in decision making.” Journal of Computational Analysis and Applications, Vol. 20, 2016, pp. 750-766.
[47] Zhou, X. and Li, Q., “Hesitant fuzzy soft set and its lattice structures.” Journal of Computational Analysis and Applications, Vol. 20, No. 1, 2016, pp. 72-80.
[48] Horel, E. and Giesecke, K., “Significance tests for neural networks.” Journal of Machine Learning Research, Nov 2020. https://arxiv.org/pdf/1902.06021.pdf
[49] Rai, A., “Explainable AI: From black box to glass box.” Journal of the Academy of Marketing Science, Vol. 48, 2020, pp. 137-141.
[50] Yang, Z., Zhang, A. and Sudjianto, A., “Enhancing explainability of neural networks through architecture constraints.” In IEEE Transactions on Neural Networks and Learning Systems, 2020.
[51] Mathew J., Griffin J., Alamaniotis M., Kanarachos S. and Fitzpatrick M. E., “Prediction of welding residual stresses using machine learning: Comparison between neural networks and neuro-fuzzy systems.” Applied Soft Computing, Vol. 70, 2018, pp. 131-146.
[52] Wan Y. and Si Y. W., “Adaptive neuro fuzzy inference system for chart pattern matching in financial time series.” Applied Soft Computing, Vol. 57, 2017, pp. 1-18.
[53] Ganeshkumar P. and Pandeeswari N., “Adaptive neuro-fuzzy-based anomaly detection system in cloud.” International Journal of Fuzzy Systems, Vol. 18, No. 3, 2016, pp. 367-378.
[54] Mizutani, E. and Jang, J. S., “Coactive neural fuzzy modeling.” Proceedings of ICNN’95 - International Conference on Neural Networks, Perth, WA, Australia, Vol. 2, 1995, pp. 760–765.
[55] Allawi, M. F., Jaafar, O., Hamzah, F. M., Mohd, N. S., Deo, R. C. and El-Shafie, A., “Reservoir inflow forecasting with a modified coactive neuro-fuzzy inference system: a case study for a semi-arid region.” Theoretical and Applied Climatology, Vol. 134, 2018, pp. 545-563.
[56] Fattahi, H., Agah, A. and Soleimanpourmoghadam, N., “Multi-output adaptive neuro-fuzzy inference system for prediction of dissolved metal levels in acid rock drainage: a case study.” Journal of AI and Data Mining, Vol. 6, No. 1, 2018, pp. 121–132.
[57] Ye B., Vynokurova O., Setlak G., Peleshko D. and Mulesa P., “Adaptive multivariate hybrid neuro-fuzzy system and its on-board fast learning.” Neurocomputing, Vol. 230, No. 22, 2017, pp. 409-416.
[58] Holland, J., Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press, 1975.
[59] Colorni, A., Dorigo, M. and Maniezzo, V., “Distributed optimization by ant colonies.” In Proceedings of the First European Conference on Artificial Life, Elsevier Publishing, 1991, pp. 134-142.
[60] Kennedy, J. and Eberhart, R., “Particle swarm optimization.” In Proceedings of ICNN’95 - International Conference on Neural Networks, Vol. 4, 1995, pp. 1942-1948.
[61] Tan, Y. and Zhu, Y., “Fireworks algorithm for optimization.” In International conference in swarm intelligence, Springer, Berlin, Heidelberg, 2010, pp. 355-364.
[62] Saremi, S., Mirjalili, S. and Lewis, A., “Grasshopper Optimisation Algorithm: Theory and application.” Advances in Engineering Software, Vol. 105, 2017, pp. 30-47.
[63] Jain, M., Singh, V. and Rani, A., “A novel nature-inspired algorithm for optimization: Squirrel search algorithm.” Swarm and Evolutionary Computation, Vol. 44, 2019, pp. 148-175.
[64] Cajori, F., “Historical note on the Newton-Raphson method of approximation.” The American Mathematical Monthly, Vol. 18, No. 2, 1911, pp. 29-32.
[65] Rumelhart, D. E., Hinton, G. E. and Williams, R. J., “Learning representations by back-propagating errors.” Nature, Vol. 323, No. 6088, 1986, pp. 533-536.
[66] Pujol, J., “The solution of nonlinear inverse problems and the Levenberg-Marquardt method.” Geophysics, Vol. 72, No. 4, 2007, pp. W1-W16.
[67] Asröm, K.J. and Wittenmark, B., Computer controlled systems: theory and design. 3rd edition, Prentice-Hall, 1997.
[68] Sabeti, S. M. N. and Deevband, M. R., “Hybrid evolutionary algorithms based on PSO-GA for training ANFIS structure.” International Journal of Computer Science Issues (IJCSI), Vol. 12, No. 5, 2015, pp. 78-86.
[69] Adibzadeh, M. and Fakharian, A., “Design and simulation of adaptive neuro fuzzy inference based controller for chaotic Lorenz system.” Journal of Computer & Robotics, Vol. 11, No. 1, 2018, pp. 15-20.
[70] Tu, C. H. and Li, C., “Multitarget prediction using an aim-object-based asymmetric neuro-fuzzy system: A novel approach.” Neurocomputing, Vol. 389, 2020, pp. 155-169.
[71] Li, C. and Tu, C. H., “Complex neural fuzzy system and its application on multi-class prediction—A novel approach using complex fuzzy sets, IIM and multi-swarm learning.” Applied Soft Computing, Vol. 84, 2019, p.105735.
[72] Li, C., “Feature selection algorithm using influence information.” National Central University, Taiwan, 2017. (unpublished draft in seminar discussion)
[73] Chiu, S., “Fuzzy Model Identification Based on Cluster Estimation.” Journal of Intelligent & Fuzzy Systems, Vol. 2, No. 3, 1994, pp. 267–278.
[74] Zhan, Z. H., Zhang, J., Li, Y. and Chung, H. S. H., ‘Adaptive particle swarm optimization’, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), Vol. 39, No. 6, 2009, pp. 1362-1381.
[75] Eibe, F., Hall, M. A. and Witten, I. H., The WEKA Workbench. Online Appendix for “Data Mining: Practical Machine Learning Tools and Techniques”, Morgan Kaufmann, 2016.
[76] Shevade, S. K., Keerthi, S. S., Bhattacharyya, C. and Murthy, K. R. K., ‘Improvements to the SMO algorithm for SVM regression’, IEEE transactions on neural networks, Vol. 11, No. 5, 2000, pp. 1188-1193.
[77] Cheng, Y. C., Li, S. T., Fuzzy time series forecasting with a probabilistic smoothing hidden Markov model. IEEE Transactions on Fuzzy Systems, Vol. 20, No. 2, 2012, pp. 291-304.
[78] Zhou, T., Chu, C., Song, S., Wang, Y., Gao, S., “A dendritic neuron model for exchange rate prediction.” In 2015 IEEE International Conference on Progress in Informatics and Computing (PIC), 2015, pp. 10-14.
[79] Ye, F., Zhang, L., Zhang, D., Fujita, H., Gong, Z., “A novel forecasting method based on multi-order fuzzy time series and technical analysis.” Information Sciences, Vol. 367, 2016, pp. 41–57.
[80] Cai, Q., Zhang, D., Wu, B., Leung, S. C., “A novel stock forecasting model based on fuzzy time series and genetic algorithm.” Procedia Computer Science, Vol. 18, 2013, pp. 1155–1162.
[81] Cai, Q., Zhang, D., Zheng, W., Leung, S. C., “A new fuzzy time series forecasting model combined with ant colony optimization and auto-regression.” Knowledge-Based Systems, Vol. 74, 2015, pp. 61–68.
指導教授 李俊賢(Chunshien Li) 審核日期 2021-7-26
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