變數選擇在智慧型系統與應用之研究

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姓名

鄭淨文(Ching-wen Cheng) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

變數選擇在智慧型系統與應用之研究
(A Study on Input Selection for Intelligence Systems and Applications)

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摘要(中)

本論文提出了輸入變數選擇(Input selection)演算法及SPSO-RLSE複合式學習演算法於複數類神經模糊系統之建模上，並將本論文方法應用於時間序列預測。複數類神經模糊系統(Complex neuro-fuzzy system, CNFS)是將複數模糊系統結合於類神經網路的一種計算模型。複數模糊集利用複數來描述隸屬程度，能增加CNFS的彈性與映射能力，在處理非線性問題如時間序列預測有良好的能力。在時間序列預測的研究中，輸入變數選擇為一重要的議題，透過輸入變數選擇，我們能找出變數之間的相依性，進而篩選出有用與重要的資訊，改善預測的準確度。本論文將實作輸入變數選擇於複數類神經模糊系統上，探討輸入變數選擇在時間序列預測中的影響。在模型的學習上，本論文使用了SPSO-RLSE複合式學習法，其將標準粒子群演算法(Standard particle swarm optimization, SPSO)用於更新系統的前鑑部參數，以及遞迴最小平方估計法(Recursive least squares estimation, RLSE)用於後鑑部參數之更新，能夠在訓練時快速的將模型最佳化已達精準預測。本論文用了5個不同的實驗來檢驗本論文方法在時間序列預測上的表現，每個實驗中都根據輸入變數選擇的結果設計了不同的輸入變數做比較以觀察輸入變數選擇的影響。實驗結果皆呈現我們提出的方法其預測準確度優於其他比較文獻的方法，證實本論文的方法在時間序列預測上有良好的表現。

摘要(英)

In this thesis, a new modeling approach has been presented, where the modeling theory of complex neuro-fuzzy system (CNFS), a Brock-Dechert-Scheinkman (BDS) based method for input selection and a SPSO-RLSE hybrid learning method for the parameter estimation of CNFS are used in the study for the problem of time series forecasting. For CNFS, complex fuzzy sets (CFSs) are embedded in the neuro-fuzzy model structure to enhance the flexibility in adaption and the non-linear ability of input-output mapping that is good for non-linear problems such as time series forecasting. A CFS is an advanced fuzzy set whose membership degrees are complex-valued and defined in the unit disc of the complex plane. For modeling and forecasting, input selection is very important, for which a variable-dependability index is used. By the results of such indices, we can find out the variable dependencies, so to select dependent variables that are related to the target variable. Afterwards, for the modeling of CNFS the SPSO-RLSE hybrid learning method applies, where the method of standard particle swarm optimization (SPSO) is used to adjust the premise parameters of CNFS and the method of recursive least squares estimation (RLSE) is used to update the consequent parameters of CNFS. This hybrid learning method can rapidly optimize the CNFS to get accurate prediction. Five examples for time series forecasting were used in the study to examine the proposed approach, whose results are compared with those by other approaches. Through the experimental results, the proposed approach shows very promising performance and outperforms the compared approaches.

關鍵字(中)

★ 複數模糊集
★ 複數類神經模糊系統
★ 標準粒子群最佳化演算法
★ 時間序列預測
★ 輸入變數選擇
★ 遞迴最小平方估計法

關鍵字(英)

★ complex fuzzy set (CFS)
★ complex neuro-fuzzy system (CNFS)
★ standard particle swarm optimization (SPSO)
★ recursive least squares estimator (RLSE)
★ time series forecasting.
★ input selection

論文目次

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 v
表目錄 vi
第 1 章緒論 1
1.1 研究背景 1
1.2 研究動機與目的 1
1.3 研究方法 2
1.4 論文架構 3
第 2 章文獻探討與理論 4
2.1 輸入變數選擇(Input Selection) 4
2.2 模糊理論與複數模糊集(Complex Fuzzy Set, CFS) 6
2.3 複數類神經模糊系統(Complex Neuro Fuzzy System, CNFS) 9
2.4 減法分群法(Subtractive Clustering) 12
2.5 標準粒子群最佳化演算法(Standard Particle Swarm Optimization, SPSO) 12
2.6 遞迴最小平方估計法(Recursive Least Squares Estimator, RLSE) 15
第 3 章系統架構 17
3.1 CNFS-ISR模型 17
3.2 系統結構學習 18
3.3 系統參數學習 19
第 4 章實驗 22
4.1 Two Henon Map 22
4.2 Three Logistic Map 26
4.3 Mackey-Glass 31
4.4 Santa Fe Laser Dataset 37
4.5 Star Brightness 41
第 5 章討論 45
第 6 章結論 47
未來研究方向 47
參考文獻 48

參考文獻

[1] R. K. Lai, C. Fan, W. Huang, P. Chang: Evolving and Clustering Fuzzy Decision Tree for Financial Time Series Data Forecasting. Expert Systems with Applications, vol. 36, no. 2, pp. 3761–3773, 2009.
[2] G. S. Atsalakis, K. P. Valavanis: Surveying Stock Market Forecasting Techniques – Part II: Soft Computing Methods. Expert Systems with Applications, vol. 36, no. 3, pp. 5932–5941, 2009.
[3] G. S. Atsalakis, K. P. Valavanis: Forecasting Stock Market Short-Term Trends Using A Neuro-Fuzzy Based Methodology. Expert Systems with Applications, vol. 36, no. 7, pp. 10696–10707, 2009.
[4] T. H. Roh: Forecasting The Volatility of Stock Price Index. Expert Systems with Applications, vol. 33, no. 4, pp. 916–922, 2007.
[5] C. Li and T. Chiang: Complex Fuzzy Computing to Time Series Prediction -A Multi-Swarm PSO Learning Approach. ACIIDS, Lecture Notes in Artificial Intelligence 6592, pp. 242–251, 2011.
[6] C. Li and T. 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.
[7] C. Li, J. Hu: A New ARIMA-based Neuro-Fuzzy Approach and Swarm Intelligence for Time Series Forecasting. Engineering Applications of Artiﬁcial Intelligence, vol. 25, no. 2, pp. 295–308, 2012.
[8] S. L. Chiu: Fuzzy Model Identification Based on Cluster Estimation. Intelligent Fuzzy Systems, vol. 2, pp. 267-278, 1994.
[9] G. Castellano, C. Castiello, A. M. Fanelli, C. Mencar: Knowledge Discovery by A Neural-Fuzzy Modeling Framework. Fuzzy Sets and Systems, vol. 149, no. 1, pp. 187–207, 2005.
[10] A. Abraham: Adaptation of Fuzzy Inference System Using Neural Learning, Fuzzy System Engineering: Theory and Practice. In Nadia Nedjah et al. (Eds.), Studies in fuzziness and soft computing, ch. 3, pp. 53–83, 2005.
[11] G. S. Atsalakis: Exchange Rate Forecasting by Neuro-Fuzzy Techniques. Journal of Financial Decision Making, vol. 1, no. 2, pp. 15–26, 2006.
[12] G. S. Atsalakis, and K. Valavanis: Neuro-Fuzzy and Technical Analysis for Stock Prediction. Working paper, Technical University of Crete, 2006.
[13] J. Kennedy and R.C. Eberhart: Particle Swarm Optimization. Proceedings of IEEE Conference on Neuro Networks, vol. 4, pp. 1942–1948, 1995.
[14] J. Kennedy: Deﬁning a Standard for Particle Swarm Optimization. IEEE Conference on Swarm Intelligence Symposium, pp.120-127, 2007.
[15] C. Lin and C. Wu: An Efficient Symbiotic Particle Swarm Optimization for Recurrent Functional Neural Fuzzy Network Design. International Journal of Fuzzy Systems, vol. 11, no. 4, 2009.
[16] C. Juang, C. Hsiao, and C. Hsu: Hierarchical Cluster-Based Multispecies Particle-Swarm Optimization for Fuzzy-System Optimization. IEEE Transactions on Fuzzy Systems, vol. 18, no. 1, 2010.
[17] P. N. Suganthan: Particle Swarm Optimizer with Neighborhood Operator. Proceedings of Conference on Evaluation Computing, pp. 1958–1962, 1999.
[18] J. Kennedy and R. Mendes: Neighborhood Topologies in Fully Informed and Best-of-Neighborhood Particle Swarms. IEEE Translation on System, Man, and Cybernetics - Part C: Applications and Reviews, vol. 36, no. 4, pp. 515–519, 2006.
[19] V. D. Bergh and A. P. Engelbrecht: A Cooperative Approach to Particle Swarm Optimization. IEEE Translation on Evaluation Computing, vol. 8, no. 3, pp. 225–239, 2004.
[20] C. J. Lin, C. H. Chen, and C. T. Lin: A Hybrid of Cooperative Particle Swarm Optimization and Cultural Algorithm for Neural Fuzzy Network and Its Prediction Applications. IEEE Translation on System, Man, and Cybernetics - Part C: Applications and Reviews, vol. 39, no. 1, pp. 55–68, 2009.
[21] Y. Shi and R. C. Eberhart: A Modiﬁed Particle Swarm Optimizer. IEEE International Conference on Evaluation Computing, pp. 69–73, 1998.
[22] M. Clerc and J. Kennedy: The Particle Swarm – Explosion, Stability, and Convergence in A Multi Dimentional Complex Space. IEEE Translation on Evaluation Computing, vol. 6, no. 1, pp. 58–73, 2002.
[23] M. Clerc: Standard Particle Swarm Optimization from 2006 to 2011.
http://www.particleswarm.info
[24] M. Clerc: Particle Swarm Optimization. International Scientific and Technical Encyclopedia, 2006.
[25] J. J. Buckley: Fuzzy Complex Numbers. Fuzzy Sets System, vol. 33, no. 3, pp. 333–345, 1989.
[26] J. J. Buckley: Fuzzy Complex Analysis II: Integration. Fuzzy Sets System, vol. 49, no. 2, pp. 171–179, 1992.
[27] J. J. Buckley and Y. Qu: Solving Linear and Quadratic Fuzzy Equations. Fuzzy Sets System, vol. 38, no.1, pp. 43–59, 1990.
[28] J. J. Buckley and Y. Qu: Solving Fuzzy Equations: A New Solution Concept. Fuzzy Sets System, vol. 39, no. 3, pp. 291–301, 1991.
[29] T. Takagi, M. Sugeno: Fuzzy Identiﬁcation of Systems and Its Applications to Modeling and Control. IEEE Translation on System, Man, and Cybernetics, vol. 15, no. 1, pp. 116–132, 1985.
[30] W.A. Brock, W.D. Dechert, J.A. Scheinkman and B. LeBaron: A Test for Independence Based on The Correlation Dimension. Econometric Reviews, vol. 15, no. 3, pp. 197-235, 1996.
[31] R. Savit and M. Green: Time Series and Dependent Variables. Physica D: Nonlinear Phenomena, vol. 50, no. 1, pp. 95-116, 1991.
[32] J. S. R. Jang, C. T. Sun, E. Mizutani: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice Hall, Upper Saddle River, NJ, USA, 1997.
[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] 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, 2011.
[35] A. Guillen, L.J. Herrera, G. Rubio, H. Pomares, A. Lendasse, I. Rojas: New Method for Instance or Prototype Selection Using Mutual Information in Time Series Prediction. Neurocomputing, vol. 73, no. 10-12, pp. 2030–2038, 2010.
[36] F. Liu, G.S. Ng, C. Quek: RLDDE: A Novel Reinforcement Learning-Based Dimension and Delay Estimator for Neuro Networks in Time Series Prediction. Neurocomputing, vol. 70, no. 7-9, pp. 1331–1341, 2007.
[37] Y. Wang, W. Jiang, S. Yuan, J. Wang: Forecasting Chaotic Time Series Based on Improved Genetic Wnn. Conference on IEEE Fourth International Natural Computation, vol. 5, pp. 519-513, 2008.
[38] R. Liu, Z. Hou, J. Shen: A Method to Determine the Parameters of Phase Space Reconstruction Based on the Neural Network. Proceeding of IEEE International Workshop on Chaos-Fractals Theories and Applications, pp. 281-285, 2009.
[39] Chih-Chung Chang and Chih-Jen Lin: Libsvm
http://www.csie.ntu.edu.tw/~cjlin/Libsvm/
[40] D. Graves, W. Pedrycz: Fuzzy Prediction Architecture Using Recurrent Neural Networks. Neurocomputing, vol. 72, no. 7-9, pp.1668-1678, 2009.
[41] Y. Deng, X. Jin, Y. Zhong: Ensemble SVR for Prediction of Time Series. Proceedings of International Conference on Machine Learning Cybernetics, Guangzhou, China, vol. 6, no. 8, pp. 3528–3534, 2005.
[42] H. Tong and K. S. Lim: Threshold Autoregression, Limit Cycles and Cyclical Data. Journal of the Royal Statistical Society: Series B, vol. 42, no. 3, pp. 245–292, 1980.
[43] A. S. Weigend, B. A. Huberman, and D. E. Rumelhart: Predicting The Future: A Connectionist Approach. International Journal of Neural Systems, vol. 1, no.3, pp. 193–209, 1990
[44] L. J. Cao: Support Vector Machines Experts for Time Series Forecasting. Neurocomputing, vol. 51, no. 1-4, pp. 321–339, 2003.
[45] H. Pi and C. Peterson: [44] L. J. Cao: Support Vector Machines Experts for Time Series Forecasting. Neurocomputing, vol. 6, no. 3, pp. 509-520, 1994.
[46] Md. R. Hassan: A Hybrid of multiobjective Evolutionary Algorithm and HMM-Fuzzy Model for Time Series Prediction. Neurocomputing, vol. 81, no.1, pp. 1-11, 2012.
[47] Adam E. Gaweda: Data-Driven Linguistic Modeling Using Relational Fuzzy Rules. IEEE Transactions on Fuzzy Systems, vol.11, no.1, pp. 121-134, 2003.
[48] S. Paul and S. Kumar: Subsethood-Product Fuzzy Neural Inference System. IEEE Transactions on Neural Networks, vol. 13, no. 3, pp. 578-599, 2002.
[49] N. Kasabov: DENFIS: Dynamic Evolving Neural-Fuzzy Inference System and Its Application for Time-Series Prediction. IEEE Transactions on Fuzzy Systems, vol. 10, no.2, pp. 144-154, 2002.
[50] X. Yao: A New Evolutionary System for Evolving Artificial Neural Networks. IEEE Transactions on Neural Networks, vol. 8, no. 3, pp. 694-713, 1997.
[51] M. Russo: Genetic Fuzzy Learning. IEEE Transactions on Evolutionary Computation, vol.4, no. 3, pp. 259-273, 2000.
[52] S. Bhardwaj and S. Srivastava: Chaotic Time Series Prediction Using Combination of Hidden Markov Model and Neural Nets. International Conference on Computer Information Systems and Industrial Management Applications (CISIM), pp. 585-589, 2010.
[53] H. Dhahri, A. M. Alimi and A. Abraham : Hierarchical Multi-Dimensional Differential Evolution for The Design of Beta Basis Function Neural Network. Neurocomputing, 2012. (in press)
[54] Y, Chena, B. Yanga and J. Dong: Time-Series Prediction Using A Local Linear Wavelet Neural Network. Neurocomputing, vol. 69, no. 4-6, pp. 449-465, 2006.
[55] A. S. Weigend and N. A. Gershenfeld: Results for Time Series Prediction Competition at The Santa Fe Institute. IEEE International Conference on Neural Network, San Francisco, CA, pp. 1786–1793, 1993.
[56] A. Lendasse, F. Corona, J. Hao, N. Reyhani, and M. Verleysen: Determination of The Mahalanobis Matrix Using Nonparametric Noise Estimation. Proceeding of European Symposium on Algorithms (ESA), pp. 227–232, 2006.
[57] G. Dangelmayr, S. Gadaleta, D. Hundley, and M. Kirby: Time Series Prediction by Estimating Markov Probabilities Through Topology Preserving Maps. Proceeding of The International Society for Optical Engineering (SPIE), vol. 3812, pp. 86–93, 1999.
[58] J. Tome and J. Carvalho: One Step Ahead Prediction Using Fuzzy Boolean Neural Networks. Proceeding of EUSFLAT-LFA, pp. 500–505, 2005.
[59] F. A. Gers, D. Eck, and J. Schmidhuber: Applying LSTM to Time Series Predictable Through Time-Window Approaches. International Conference on Artificial Neural Network, pp. 669–676, 2001.
[60] E. A. Wan: Time Series Prediction by Using A Connectionist Network With Internal Time Delays. Time Series Prediction: Forecasting the Future and Understanding the Past, MA: Addison Wesley, pp. 195–217, 1994.
[61] T. Koskela, M. Varsta, J. Heikkonen, and K. Kaski: Recurrent SOM with Local Linear Models in Time Series Prediction. Proceeding of European Symposium on Algorithms (ESA), pp. 167–172, 1998.
[62] R. Bakker, J. C. Schouten, C. L. Giles, F. Takens, and C. M. Van Den Bleek: Learning Chaotic Attractors by Neural Networks. Neural Computing. vol. 12, no. 10, pp. 2355–2383, 2000.

指導教授

李俊賢(Chunshien Li)

審核日期

2012-7-22

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