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姓名江泰緯(Tai-Wei Chiang) 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模式使用複數模糊集合與ARIMA模型

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摘要(中)由於複數模糊集合 (Complex fuzzy set, CFS) 理論的提出，為模糊系統與其相關的研究帶來一個新的視野。雖然CFS的概念與特性已經被提出探討，但仍未有研究提出一套明確的CFS設計準則與方法，而且運用此概念之相關研究仍然相當少。因此，本研究提出一個新的複數模糊類神經系統 (Complex neuro-fuzzy system, CNFS)，結合複數模糊集合、類神經模糊系統 (neuro-fuzzy system, NFS) 以及自回歸移動平均模型 (Auto-regressive integrated moving average, ARIMA) 並應用於系統建模之研究。本研究提出新的高斯複數模糊集合 (Gaussian CFSs) 來描述模糊法則之前鑑部 (Premise parts)，ARIMA 模型作為法則之後鑑部 (Consequent parts)。複數模糊集合是從模糊集合延伸而來，其歸屬程度可進一步延伸至單位複數圓盤，因而增加歸屬函數描述的能力。藉由複數模糊集合之特性，CNFS-ARIMA模型擁有良好的非線性映射能力。由於 CNFS 模型之輸出為一個複數值，其實部 (Real part) 與虛部 (Imaginary part) 可同時用來處理不同函數之映射，此可稱為雙輸出之特性 (Dual-output property)。為了構建 CNFS-ARIMA 模型，本研究將透過結構學習與參數學習方法來自我建構與調整 CNFS-ARIMA 模型之結構與參數。在結構學習階段，本研究使用模糊 C 平均分裂演算法來自動決定符合樣本資料分布特性的系統架構與法則數目；參數學習階段使用粒子群最佳化演算法 (Particle swarm optimization, PSO) 與遞迴式最小平方估計器 (Recursive least squares estimator, RLSE），稱為 PSO-RLSE 複合式學習演算法，進行系統參數之快速學習。為了測試本研究所提出之方法的效能，本研究使用過去研究常用之標竿系統建模之資料集作為實驗範例，並與文獻所提出之方法進行比較。本研究運用函數逼近與實際的財務經濟時間序列資料，來測試模型雙輸出之實驗。由實驗結果可證實本研究所提出之系統方法可以獲得良好的效能。 摘要(英)Ever since the initiate of the theory of complex fuzzy sets (CFSs), a new vision has dawned upon fuzzy systems and their variants. Although there has been considerable development made in determining the properties of CFSs, the research on complex fuzzy system designs and applications of this concept is found rarely. In this dissertation, we present a novel self-organizing complex neuro-fuzzy intelligent approach using CFSs for the applications of system modeling. The proposed approach integrates a complex neuro-fuzzy system (CNFS) using CFSs and auto-regressive integrated moving average (ARIMA) models to form the proposed computing model, called the CNFS-ARIMA. A class of Gaussian complex fuzzy sets is proposed to describe the premise parts of fuzzy If-Then rules, whose consequent parts are specified by ARIMA models. A CFS is an advanced fuzzy set whose membership degrees are complex-valued within the unit disc of the complex plane, expanding the capability of membership description. With the nature of CFS, the proposed CNFS models have excellent nonlinear mapping capability. Moreover, the output of CNFS-ARIMA is complex-valued, of which the real and imaginary parts can be used for two different functional mappings, respectively. This is the so-called dual-output property. For the formation of CNFS-ARIMA, structure learning and parameter learning are involved to self-organize and self-tune the proposed model. For the structure learning phase, a FCM-based splitting algorithm (FBSA) is used to automatically determine the initial knowledge base of the CNFS-ARIMA. The PSO-RLSE hybrid learning algorithm is proposed for the purpose of fast learning, integrating the particle swarm optimization (PSO) and the recursive least squares estimator (RLSE). A number examples of time series are used to test the proposed approach, whose results are compared with those by other approaches. Moreover, real-world applications of system modeling including function approximation and time series are used for the proposed approach to perform the dual-output forecasting experiments. The experimental results indicate that the proposed approach shows excellent performance. 關鍵字(中)★ 複數模糊集合

★ 複數模糊類神經系統

★ 自回歸平均移動模型

★ 模糊C平均分裂演算法

★ 粒子群演算法

★ 遞迴式最小平方估計器關鍵字(英)★ Complex fuzzy set

★ Complex neuro-fuzzy system (CNFS)

★ Autoregressive integrated moving average (ARIMA)

★ FCM-based splitting algorithm (FBSA)

★ Particle swarm optimization (PSO)

★ Recursive least squares estimator (RLSE)論文目次中文摘要 ………………………….…………………………………………i

Abstract ……………………….…………………………………………..iii

致謝 ……………………….……………………………………………v

Contents …………………….……………………………………………..vi

Table List …………………….………………………………………………ix

Figure List ……………….…………………………………………………..xi

Symbol List …………………………………………………………………...xv

Abbreviations List xvii

Chapter 1. Introduction 1

1.1. Research background and motivation 1

1.2. Research objectives 6

1.3. Research contribution 9

1.4. Organization of the dissertation 10

Chapter 2. Related Works 11

2.1. Complex fuzzy set 11

2.1.1. Definition of complex fuzzy set 11

2.1.2. A literature review of complex fuzzy set 12

2.2. Neuro-fuzzy systems 14

2.3. ARIMA: autoregressive integrated moving average model 17

Chapter 3. Methodology for Complex Neuro-Fuzzy System 20

3.1. Gaussian complex fuzzy set 20

3.2. CNFS-ARIMA: complex neuro-fuzzy system based ARIMA 23

Chapter 4. Machine Learning for the Proposed CNFS-ARIMA 28

4.1. Structure learning 28

4.2. Parameter learning 32

Chapter 5. Experimentation 37

5.1. Example 1. Dual-Function Approximation for Two Nonlinear Functions by CNFS 37

5.2. Example 2. Star Brightness Time Series 43

5.3. Example 3. Mackey-Glass Chaos Time Series 54

5.4. Example 4. Dual Time Series of Daily NASDAQ Composite Index 61

5.5. Example 5. Dual Time Series of TAIEX and DJI 65

Chapter 6. Discussions 72

6.1. Discussion of the experimental results 72

6.2. Discussion of the proposed machine learning method 76

6.3. Discussion of the computing overhead of the proposed approach 78

Chapter 7. Conclusion and Future Work 83

References …………………………………………………………………….85

Vitae …………………………………………………………………...102參考文獻[1] K. Huarng and T. H.-K. Yu, " The application of neural networks to forecast fuzzy time series," Physica A: Statistical Mechanics and its Applications, vol. 363, no. 2, pp. 481-491, 2006.

[2] D. Ramot, M. Friedman, G. Langholz and A. Kandel, "Complex fuzzy logic," IEEE Transactions on Fuzzy Systems, vol. 11, no. 4, pp. 450-461, 2003.

[3] G. Zhang, B. E. Patuwo and M. Y. Hu, "Forecasting with artificial neural networks: the state of the art," International Journal of Forecasting, vol. 14, no. 1, pp. 35-62, 1998.

[4] W. R. Foster, F. Collopy and L. H. Ungar, " Neural network forecasting of short, noisy time series," Computers & Chemical Engineering, vol. 16, no. 4, pp. 293-297, 1992.

[5] M.-C Tan, S. C. Wong, J.-M Xu, Z.-R Guan and P. Zhang, "An aggregation approach to short-term traffic flow prediction," IEEE Transactions on Intelligent Transportation Systems, vol. 10, no. 1, pp. 60-69, 2009.

[6] S. L. Ho and M. Xie, "The use of ARIMA models for reliability forecasting and analysis," Computers & Industrial Engineering, vol. 35, no. 1-2, pp. 213-216, 1998.

[7] V. Ş. Ediger and S. Akar, "ARIMA forecasting of primary energy demand by fuel in Turkey," Energy Policy, vol. 35, no. 3, pp. 1701-1708, 2007.

[8] G. P. Zhang, " Time series forecasting using a hybrid ARIMA and neural network model," Neurocomputing, vol. 50, pp. 159-175, 2003.

[9] I. Rojas, O. Valenzuela, F. Rojas, A. Guillen, L. J. Herrera, H. Pomares, L. Marquez and M. Pasadas, "Soft-computing techniques and ARMA model for time series prediction," Neurocomputing, vol. 71, no. 4-6, pp. 519-537, 2008.

[10] D. Graves and W. Pedrycz, "Fuzzy prediction architecture using recurrent neural networks," Neurocomputing, vol. 72, no. 7-9, pp. 1668-1678, 2009.

[11] C. Li and R. Priemer, "Fuzzy control of unknown multiple-input-multiple-output plants," Fuzzy Sets and Systems, vol. 104, no. 2, pp. 245-267, 1999.

[12] C. Li, C.-Y. Lee and K.-H. Cheng, "Pseudoerror-based self-organizing neuro-fuzzy system," IEEE Transactions on Fuzzy Systems, vol. 12, no. 6, pp. 812-819, 2004.

[13] C. Li, and R. Priemer, "Self-learning general purpose PID controller," Journal of the Franklin Institute, vol. 334, no. 2, pp. 167-189, 1997.

[14] C. Li and C.-Y. Lee, "Self-organizing neuro-fuzzy system for control of unknown plants," IEEE Transactions on Fuzzy Systems, vol. 11, no. 1, pp. 135-150, 2003.

[15] P. Chen, T. Pedersen, B.-J Birgitte and Z. Chen, "ARIMA-based time series model of stochastic wind power generation," IEEE Transactions on Power Systems, vol. 25, no. 2, pp. 667-676, 2010.

[16] D. Ramot, R. Milo, M. Friedman and A. Kandel, "Complex fuzzy sets," IEEE Transactions on Fuzzy Systems, vol. 10, no. 2, pp. 171-186, 2002.

[17] D. Ramot, M. Friedman, G. Langholz, R. Milo, and A. Kandel, "On complex fuzzy sets," in the 10th IEEE International Conference on Fuzzy Systems, pp. 1160-1163, 2001.

[18] G. Zhang,T. S. Dillon, K.-Y. Cai, J. Ma and J. Lu, "Operation properties and -equalities of complex fuzzy sets," International Journal of Approximate Reasoning, vol. 50, no. 8, pp. 1227-1249, 2009.

[19] Z. Chen, S. Aghakhani, J. Man, and S. Dick, "ANCFIS: a neurofuzzy architecture employing complex fuzzy sets," IEEE Transactions on Fuzzy Systems, vol. 19, no. 2, pp. 305-322, 2011.

[20] J. Ma, G. Zhang and J. Lu "A method for multiple periodic factor prediction problems using complex fuzzy sets," IEEE Transactions on Fuzzy Systems, vol. 20, no. 1, pp. 32-45, 2012.

[21] G. E. P. Box, G. M. Jenkins and G. C. Reinsel, Time Series Analysis: Forecasting and Control, 4th ed., Prentice Hall, Englewood Cliffs, NJ, USA, 2008.

[22] P.-F. Pai and C.-S. Lin, "A hybrid ARIMA and support vector machines model in stock price forecasting," Omega, vol. 33, no. 6, pp. 497-505, 2005.

[23] M. C. Mackey and L. Glass, "Oscillation and chaos in physiological control systems," Science, vol. 197, no. 4300, pp. 287-289, 1977.

[24] P. C. Nayak, K. P. Subheer, D. M. Rangan and K. S. Ramasastri, "A neuro-fuzzy computing technique for modeling hydrological time series," Journal of Hydrology, vol. 291, no. 6, pp. 52-66, 2004.

[25] A. Jain and A. M. Kumar, "Hybrid neural network models for hydrologic time series forecasting," Applied Soft Computing, vol. 7, no. 2, pp. 585-592, 2007.

[26] L. Yu, S. Wang and K. K. Lai, "A neural-network-based nonlinear metamodeling approach to financial time series forecasting," Applied Soft Computing, vol. 9, no. 2, pp. 563-574, 2009.

[27] W.-K. Wong, E. Bai and A. W. Chu, "Adaptive time-variant models for fuzzy-time-series forecasting," IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 40, no. 6, pp. 1531-1542, 2010.

[28] M. A. Boyacioglu and D. Avci, "An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: the case of the Istanbul stock exchange," Expert Systems with Applications, vol. 37, no. 12, pp. 7908-7912, 2010.

[29] B. Wang, S. Wang and J. Watada, "Fuzzy-Portfolio-Selection Models With Value-at-Risk," IEEE Transactions on Fuzzy Systems, vol. 19, no. 4, pp. 758 - 769, 2011.

[30] Y.-C. Cheng and S.-T. Li, "Fuzzy time series forecasting with a probabilistic smoothing hidden Markov model," IEEE Transactions on Fuzzy Systems, vol. 20, no. 2, pp. 291-304, 2012.

[31] K. Hornik, M. Stinchcombe and H. White, "Multilayer feedforward networks are universal approximators," Neural Networks, vol. 2, no. 5, pp. 359-366, 1989.

[32] J. L. Castro, "Fuzzy logic controllers are universal approximators," IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, vol. 25, no. 4, pp. 629-635, 1995.

[33] J. S. R. Jang, "ANFIS: adaptive-network-based fuzzy inference system," IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, no. 3, pp. 665-685, 1993.

[34] T. Taskaya-Temizel and M. C. Casey, "A comparative study of autoregressive neural network hybrids," Neural Networks, vol. 18, no. 5-6, pp. 781-789, 2005.

[35] Y. Gao and M. J. Er, "NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches," Fuzzy Sets and Systems, vol. 150, no. 2, pp. 331-350, 2005.

[36] O. Valenzuela, I. Rojas, F. Rojas, H. Pomares, L. J. Herrera, A. Guillen, L. Marquez and M. Pasadas "Hybridization of intelligent techniques and ARIMA models for time series prediction," Fuzzy Sets and Systems, vol. 159, no. 7, pp. 821-845, 2008.

[37] M. Khashei and M. Bijari, "A novel hybridization of artificial neural networks and ARIMA models for time series forecasting," Applied Soft Computing, vol. 11, no. 2, pp. 2664-2675, 2011.

[38] C. Li and T.-W. Chiang, "Complex fuzzy computing to time series prediction- a multi-swarm PSO learning approach," Lecture Notes in Artificial Intelligence, vol. 6592, pp. 242-251, 2011.

[39] K. Chakraborty, K. G. Mehrotra, C. K. Mohan, S.Ranka,“Forecasting the behavior of multivariate time series using neural networks,”Neural Networks, vol. 5, no. 6, pp. 961-970, 1992.

[40] H. Raman and N. Sunilkumar, "Multivariate modelling of water resources time series using artificial neural networks," Hydrological Sciences Journal, vol. 40, no. 2, pp. 145-163, 1995.

[41] J. Nie, “Nonlinear time-series forecasting: A fuzzy-neural approach,”Neurocomputing, vol. 16, pp. 63-76, 1997.

[42] J. C. Ochoa-Rivera, R. Garcia-Bartual and J. Andreu , "Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks," Hydrology and Earth System Sciences Discussions, vol. 6, no.4, pp. 641-654, 2002.

[43] H. Sun, S. Wang and Q. Jiang, "FCM-based model selection algorithms for determining the number of clusters," Pattern Recognition, vol. 37, no. 10, pp. 2027-2037, 2004.

[44] R. Eberhart and J. Kennedy, "A new optimizer using particle swarm theory," in Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39-43, 1995.

[45] J. Kennedy and R. Eberhart, "Particle swarm optimization," in IEEE International Conference on Neural Networks Proceedings, vol. 4, pp. 1942-1948, 1995.

[46] S. Yuhui and R. C. Eberhart, " Fuzzy adaptive particle swarm optimization," in Proceedings of the 2001 Congress on Evolutionary Computation, vol. 1, pp. 101-106, 2001.

[47] C.-F. Juang, "A hybrid of genetic algorithm and particle swarm optimization for recurrent network design," IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 34, no. 2,pp. 997-1006, 2004.

[48] A. Chatterjee, K. Pulasinghe, K. Watenabe and K. Izumi, "A particle-swarm-optimized fuzzy-neural network for voice-controlled robot systems," IEEE Transactions on Industrial Electronics, vol. 52, no. 6, pp. 1478-1489, 2005.

[49] Y.-X. Liao, J.-H. She and M. Wu, "Integrated hybrid-PSO and fuzzy-NN decoupling control for temperature of reheating furnace," IEEE Transactions on Industrial Electronics, vol. 56, no. 7, pp. 2704-2714, 2009.

[50] C.-F. Juang, C.-M. Hsiao and C.-H. Hsu, "Hierarchical cluster-based multispecies particle-swarm optimization for fuzzy-system optimization," IEEE Transactions on Fuzzy Systems, vol. 18, no. 1, pp. 14-26, 2010.

[51] R. P. Prado, S. Garcia-Galan, J. E. M. Exposito and A. J. Yuste, "Knowledge acquisition in fuzzy-rule-based systems with particle-swarm optimization," IEEE Transactions on Fuzzy Systems, vol. 18, no. 6, pp. 1083-1097, 2010.

[52] L. Ljung and E. Ljung, System Identification: Theory for the User. NJ, Prentice-Hall Englewood Cliffs, 1987.

[53] S. J. Nanda, G. Panda and B. Majhi, "Improved identification of Hammerstein plants using new CPSO and IPSO algorithms," Expert Systems with Applications, vol. 37, no. 10, pp. 6818-6831, 2010.

[54] S.-M. Chen and C.-D. Chen, "TAIEX forecasting based on fuzzy time series and fuzzy variation groups," IEEE Transactions on Fuzzy Systems, vol. 19, no. 1, pp. 1-12, 2011.

[55] J. J. Buckley, "Fuzzy complex numbers," Fuzzy Sets and Systems, vol. 33, no. 3, pp. 333-345, 1989.

[56] J. J. Buckley and Y. Qu, "Fuzzy complex analysis I: differentiation," Fuzzy Sets and Systems, vol. 41, no. 3, pp. 269-284, 1991.

[57] J. J. Buckley, "Fuzzy complex analysis II: integration," Fuzzy Sets and Systems, vol. 49, no. 2, pp. 171-179, 1992.

[58] D. Moses, O. Degani, H. N. Teodorescu, M. Friedman and A. Kandel, "Linguistic coordinate transformations for complex fuzzy sets," in IEEE InternationalFuzzy Systems Conference Proceedings, vol. 3, pp. 1340-1345, 1999.

[59] S. Dick, "Toward complex fuzzy logic," IEEE Transactions on Fuzzy Systems, vol. 13, no. 3, pp. 405-414, 2005.

[59] D. Qiu, L. Shu and Z.-W. Mo, " Notes on fuzzy complex analysis," Fuzzy Sets and Systems, vol. 160, no. 11, pp. 1578-1589, 2009.

[61] A. Ghosh, B. U. Shankar and S. K. Meher, "A novel approach to neuro-fuzzy classification," Neural Networks, vol. 22, pp. 100-109, 2009.

[62] A. F. Cabalar, A. Cevik and C. Gokceoglu, "Some applications of Adaptive Neuro-Fuzzy Inference System (ANFIS) in geotechnical engineering," Computers and Geotechnics, vol. 40, pp. 14-33, 2012.

[63] K.-I. Funahashi, "On the approximate realization of continuous mappings by neural networks," Neural Networks, vol. 2, no. 3, pp. 183-192, 1989.

[64] G. Li, J. Shi and J. Zhou, "Bayesian adaptive combination of short-term wind speed forecasts from neural network models," Renewable Energy, vol. 36, no. 1, pp. 352-359, 2011.

[64] G. Li, J. Shi and J. Zhou, "Bayesian adaptive combination of short-term wind speed forecasts from neural network models," Renewable Energy, vol. 36, no. 1, pp. 352-359, 2011.

[65] X. Liang, H. Zhang, J. Xiao, and Y. Chen , "Improving option price forecasts with neural networks and support vector regressions," Neurocomputing, vol. 72, pp. 3055-3065, 2009.

[66] C.-H. Wei and Y. Lee, "Sequential forecast of incident duration using Artificial Neural Network models," Accident Analysis & Prevention, vol. 39, no. 5, pp. 944-954, 2007.

[67] G. Arulampalam and A. Bouzerdoum, "A generalized feedforward neural network architecture for classification and regression," Neural Networks, vol. 16, no. 5-6, pp. 561-568, 2003.

[68] K. A. de Oliveira, Á. Vannucci, E. C. da Silva, "Using artificial neural networks to forecast chaotic time series," Physica A: Statistical Mechanics and its Applications, vol. 284, no.1-4, pp. 393-404, 2000.

[69] L. A. Zadeh, "Fuzzy sets," Information and Control, vol. 8, pp. 338-353, 1965.

[70] E. S. Tognetti, R. C.L.F. Oliveira, P. L.D. Peres, " Reduced-order dynamic output feedback control of continuous-time T–S fuzzy systems," Fuzzy Sets and Systems, vol. 207, pp. 27-44, 2012.

[71] Y.-Q. Yao, J.-S. Mi and Z.-J Li, "Attribute reduction based on generalized fuzzy evidence theory in fuzzy decision systems," Fuzzy Sets and Systems, vol. 170, no. 1, pp. 64-75, 2011.

[72] F. Liu, M. Wu, Y. He and R. Yokoyama, "New delay-dependent stability criteria for T–S fuzzy systems with time-varying delay," Fuzzy Sets and Systems, vol. 161, pp. 2033-2042, 2010.

[73] J. M. Andújar and A. J. Barragán, "A methodology to design stable nonlinear fuzzy control systems," Fuzzy Sets and Systems, vol. 154, pp. 157-181, 2005.

[74] D. Nauck and R. Kruse, "Neuro-fuzzy systems for function approximation," Fuzzy Sets and Systems, vol. 101, pp. 261-271, 1999.

[75] D. Nauck and R. Kruse, "A neuro-fuzzy method to learn fuzzy classification rules from data," Fuzzy Sets and Systems, vol. 89, pp. 277-288, 1997.

[76] T. Faisal, M. N. Taib and F. Lbrahim, "Adaptive Neuro-Fuzzy Inference System for diagnosis risk in dengue patients," Expert Systems with Applications, vol. 39, pp. 4483-4495, 2012.

[77] P. Provenzano, S. Ferlisi and A. Musso, "Interpretation of a model footing response through an adaptive neural fuzzy inference system," Computers and Geotechnics, vol. 31, no. 3, pp. 251-266, 2004.

[78] Q. Zhou, C. W. Chan and P. Tontiwachwuthikul, "An application of neuro-fuzzy technology for analysis of the capture process," Fuzzy Sets and Systems, vol. 161, pp. 2597-2611, 2010.

[79] W.-P. Wang and Z. Chen, "A neuro-fuzzy based forecasting approach for rush order control applications," Expert Systems with Applications, vol. 35, pp. 223-234, 2008.

[80] K. Xu, G Zhang and Y Xu, "Adjustment strategy for dynamic tracking neuro-fuzzy controller," Procedia Engineering, vol. 23, pp. 29-33, 2011.

[81] J. Liu, W. Wang, F. Golnaraghi and E. Kubica, "A neural fuzzy framework for system mapping applications," Knowledge-Based Systems, vol. 23, no. 6, pp. 572-579, 2010.

[82] Y.-H. Chien, W.-Y. Wang, Y.G. Leu and T.T. Lee, "Robust adaptive controller design for a class of uncertain nonlinear systems using online T–S fuzzy-neural modeling approach," IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 41, pp. 542-552, 2011.

[83] S.-B. Roh, S.K. Oh and W. Prdrycz, "A fuzzy ensemble of parallel polynomial neural networks with information granules formed by fuzzy clustering," Knowledge-Based Systems, vol. 23, no.3, pp. 202-219, 2010.

[84] M.-F. Han, C.-T. Lin, J.-Y. Chang, "Differential evolution with local information for neuro-fuzzy systems optimisation," Knowledge-Based Systems, vol. 44, pp. 78-89, 2013.

[85] C.-F. Juang and C.-F. Lu, "Combination of online clustering and Q-value based genetic reinforcement learning for fuzzy network design," Proceedings of the International Joint Conference on Neural Networks, pp. 1885-1890 vol.3, 2003

[86] F. Hoffmann,D. Schauten and S. Holemann, "Incremental evolutionary design of TSK fuzzy controllers," IEEE Transactions on Fuzzy Systems, vol. 15, no. 4, pp. 563-577, 2007.

[87] C. Li and T.-W. Chiang, "Complex fuzzy model with PSO-RLSE hybrid learning approach to function approximation," International Journal of Intelligent Information and Database Systems, vol. 5, no.4, pp. 409-430, 2011.

[88] C. Li, T.-W. Chiang, J.-W. Hu and T. Wu, "Complex neuro-fuzzy intelligent approach to function approximation," in 2010 Third International Workshop on Advanced Computational Intelligence (IWACI), pp. 151-156, 2010.

[89] C. Li and T.-W. Chiang, "Function approximation with complex neuro-fuzzy system using complex fuzzy sets – a new approach," New Generation Computing, vol. 29, no. 3, pp. 261-276, 2011.

[90] J. C. Dunn, "A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters," Cybernetics and Systems, vol. 3, no. 3, pp. 32-57, 1973.

[91] J. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. MA, Kluwer Academic Publishers Norwell, 1981.

[92] C. Li, T.-W. Chiang and L.-C. Yeh, "A novel self-organizing complex neuro-fuzzy approach to the problem of time series forecasting," Neurocomputing, vol. 99, no. 1, pp. 467-476, 2013.

[93] J. Kim and N. Kasabov, "HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems," Neural Networks, vol. 12, no. 9, pp. 1301-1319, 1999.

[94] D. S. Broomhead and D. Lowe, “Multivariable functional interpolation and adaptive networks,” Complex Systems, vol. 2, pp. 321-355, 1988.

[95] N. I. Sapankevychand and R. Sankar, ”Time series prediction using support vector machines: a survey,” IEEE Computational Intelligence Magazine, vol. 4, no. 2, pp.24-38, 2009.

[96] J. Luts, F. Ojeda, R. V. de Plas, B. D. Moor, S. V. Huffel and A. K. Suykens "A tutorial on support vector machine-based methods for classification problems in chemometrics," Analytica Chimica Acta, vol. 665, no. 2, pp. 129-145, 2010.

[97] C.-C. Chang and C.-J. Lin, "LIBSVM: A library for support vector machines," ACM Trans. Intell. Syst. Technol., vol. 2, pp. 1-27, 2011.

[98] D. E. Gustafson and W. C. Kessel, "Fuzzy clustering with a fuzzy covariance matrix," in Proceedings of IEEE Conference on Decision and Control, pp. 761-766, 1979.

[99] K. B. Cho and B. H. Wang, "Radial basis function based adaptive fuzzy systems and their applications to system identification and prediction," Fuzzy Sets and Systems, vol. 83, no. 3, pp. 325-339, 1996.

[100] S. Paul and S. Kumar, "Subsethood-product fuzzy neural inference system (SuPFuNIS)," IEEE Transactions on Neural Networks, vol. 13, no. 3, pp. 578-599, 2002.

[101] X. Deng and X. Wang, "Incremental learning of dynamic fuzzy neural networks for accurate system modeling," Fuzzy Sets and Systems, vol. 160, no. 7, pp. 972-987, 2009.

[102] Y. Chen, B. Yang, J. Dong and A. Abraham , "Time-series forecasting using flexible neural tree model," Information Sciences, vol. 174, pp. 219-235, 2005.

[103] Y.- F. Deng, X. Jin and Y.-X. Zhong, "Ensemble SVR for prediction of time series," in Proceedings of 2005 International Conference on Machine Learning and Cybernetics, vol. 6,pp. 3528-3534, 2005.

[104] Y Chen, B Yang, J Dong, "Time-series prediction using a local linear wavelet neural network," Neurocomputing, vol. 69, no. 4-6, pp. 449-465, 2006.

[105] R. S. Crowder, "Predicting the Mackey–Glass time series with cascade-correlation learning," in Proceedings of 1990 Summer School Connectionist Models, D. S. Touretzky, J. L. Elman, T. J. Sejnowski, and G. E. Hinton, Eds. San Francisco, CA: Morgan Kaufmann, pp. 117-123, 1990.

[106] Website of Yahoo Finance: NASDAQ Composite Index. Available: http://finance.yahoo.com/q?s=^IXIC.

[107] Website of Yahoo Finance: Dow Jones Industrial Average Index. Available: http://finance.yahoo.com/q?s=^DJI.

[108] Website of Yahoo Finance: Taiwan Stock Exchange Capitalization Weighted Stock Index. Available: http://finance.yahoo.com/q?s=^TWII.

[109] S.-M. Chen, "Forecasting enrollments based on fuzzy time series," Fuzzy Sets and Systems, vol. 81, no. 3, pp. 311-319, 1996.

[110] K.-H. Huarng, T. H.-K. Yu and Y. W. Hsu, "A multivariate heuristic model for fuzzy time-series forecasting," IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 37, no. 4, pp. 836-846, 2007.

[111] T. H.-K. Yu and K.-H. Huarng, "A bivariate fuzzy time series model to forecast the TAIEX," Expert Systems with Applications, vol. 34, no. 4, pp. 2945-2952, 2008.

[112] T. H.-K. Yu and K.-H. Huarng, "Corrigendum to “A bivariate fuzzy time series model to forecast the TAIEX” [Expert Systems with Applications 34 (4) (2010) 2945–2952]," Expert Systems with Applications, vol. 37, no. 7, p. 5529, 2010.

[113] M. Clerc and J. Kennedy, " The particle swarm - explosion, stability, and convergence in a multidimensional complex space," IEEE Transactions on Evolutionary Computation, vol. 6, no. 1, p. 58-73, 2002.指導教授李俊賢(Chunshien Li) 審核日期2013-12-25 推文facebook plurk twitter funp google live udn HD myshare reddit netvibes friend youpush delicious baidu 網路書籤Google bookmarks del.icio.us hemidemi myshare