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Neural Computing and Applications

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01.03.2011 | Original Article

Fuzzy wavelet neural network based on fuzzy clustering and gradient techniques for time series prediction

verfasst von: Rahib H. Abiyev

Erschienen in: Neural Computing and Applications | Ausgabe 2/2011

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Abstract

This paper presents the development of fuzzy wavelet neural network system for time series prediction that combines the advantages of fuzzy systems and wavelet neural network. The structure of fuzzy wavelet neural network (FWNN) is proposed, and its learning algorithm is derived. The proposed network is constructed on the base of a set of TSK fuzzy rules that includes a wavelet function in the consequent part of each rule. A fuzzy c-means clustering algorithm is implemented to generate the rules, that is the structure of FWNN prediction model, automatically, and the gradient-learning algorithm is used for parameter identification. The use of fuzzy c-means clustering algorithm with the gradient algorithm allows to improve convergence of learning algorithm. FWNN is used for modeling and prediction of complex time series and prediction of foreign-exchange rates. Exchange rates are dynamic process that changes every day and have high-order nonlinearity. The statistical data for the last 2 years are used for the development of FWNN prediction model. Effectiveness of the proposed system is evaluated with the results obtained from the simulation of FWNN-based systems and with the comparative simulation results of previous related models.

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Box GEP (1970) Time series analysis, forecasting and control. Holden Day, San FranciscoMATH

So MKP, Lam K, Li WK (1999) Forecasting exchange rate volatility using autoregressive random variance model. Appl Financial Econ 9:583–591CrossRef

Hsieh DA (1989) Modeling heteroscedasticity in daily foreign-exchange rates. J Bus Econ Stat 7:307–317CrossRef

Bollerslev T (1990) Modeling the coherence in short-run nominal exchange rates: a multivariate generalized ARCH model. Rev Econ Stat 72:498–505CrossRef

Huang W, Lai KK, Nakamori Y, Wang S (2004) Forecasting the foreign exchange rates with artificial neural networks: a review. Intl J Inf Tech Decis Mak 3(1):145–165CrossRef

Maddala GS (1996) Introduction to econometrics. Prentice-Hall, Englewood Cliffs

Chen Y, Yang B, Dong J, Abraham A (2004) Nonlinear system modelling via optimal design of neural trees. Int J Neural Syst 14(2):125–137CrossRef

Kim K-j, Lee WB (2004) Stock market prediction using artificial neural networks with optimal feature transformation. Neural Comput Appl 13(3):255–260CrossRef

Sfetsos A, Siriopoulos C (2004) Combinatorial time series forecasting based on clustering algorithms and neural networks. Neural Comput Appl 13(1):56–64CrossRef

10.

Maqsood I, Khan MR, Abraham A (2004) An ensemble of neural networks for weather forecasting. Neural Comput Appl 13(2):112–122

11.

Thomas B, Soleimani-Mohseni M (2007) Artificial neural network models for indoor temperature prediction: investigations in two buildings. Neural Comput Appl 16(1):81–89

12.

Górriz JM, Puntonet CG, Salmerón M, de la Rosa JJG (2004) A new model for time-series forecasting using radial basis functions and exogenous data. Neural Comput Appl 13(2):101–111

13.

Sun YF, Liang YC, Zhang WL, Lee HP, Lin WZ, Cao LJ (2005) Optimal partition algorithm of the RBF neural network and its application to financial time series forecasting. Neural Comput Appl 14(1):36–44CrossRef

14.

Garg S, Pal SK (2007) Evaluation of the performance of backpropagation and radial basis function neural networks in predicting the drill flank wear. Neural Comput Appl 16(4–5):407–417

15.

Hocaoglu FO, Oysal Y, Kurban M (2009) Missing wind data forecasting with adaptive neuro-fuzzy inference system. Neural Comput Appl 18(3):207–212

16.

Gholipour A, Lucas C, Araabi BN, Mirmomeni M, Shafiee M (2007) Extracting the main patterns of natural time series for long-term neurofuzzy prediction. Neural Comput Appl 16(4–5):383–393

17.

Weigend AS, Huberman BA, Rumelhart DE (1992) Predicting sunspots and exchange rates with connectionist networks. In: Casdagli M, Eubank S (eds) Nonlinear modeling and forecasting. Addison-Wesley, Redwood City, CA, pp 395–432

18.

Refenes AN (1993) Constructive learning and its application to currency exchange rate forecasting. In: Trippi R, Turban E (eds) Neural networks in finance and investing: using artificial intelligence to improve real-world performance. Probus, Chicago, pp 777–805

19.

Refenes AN, Azema-Barac M, Chen L, Karoussos SA (1993) Currency exchange rate prediction and neural network design strategies. Neural Comput Appl 1:46–58CrossRef

20.

Kuan CM, Liu T (1995) Forecasting exchange rates using feedforward and recurrent neural networks. J Appl Econom 10:347–364CrossRef

21.

Hann TH, Steurer E (1996) Much ado about nothing? Exchange rate forecasting: neural networks versus linear models using monthly and weekly data. Neurocomputing 10:323–339MATHCrossRef

22.

Episcopos A, Davis J (1996) Predicting returns on Canadian exchange rates with artificial neural networks and EGARCHM-M model. Neural Comput Appl 4:168–174CrossRef

23.

Yager RR, Zadeh LA (eds) (1994) Fuzzy sets, neural networks and soft computing. Van Nostrand Reinhold, New YorkMATH

24.

Jang J-SR, Sun Ch-T, Muzutani E (1997) Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence. Prentice Hall, Upper Saddle River, NJ

25.

Kugarajah T, Zhang Q (1995) Multidimensional wavelet frames. IEEE Trans Neural Netw 6:1552–1556CrossRef

26.

Zhang Q, Benviste A (1995) Wavelet networks. IEEE Trans Neural Netw 3:889–898CrossRef

27.

Zhang J, Walter GG, Wayne Lee WN (1995) Wavelet neural networks for function learning. IEEE Trans Signal Process 43(6):1485–1497CrossRef

28.

Postalcioglu S, Becerikli Y (2007) Wavelet networks for nonlinear system modelling. Neural Comput Appl 16(4–5):433–441

29.

Lotric U, Dobnikar A (2005) Predicting time series using neural networks with wavelet-based denoising layers. Neural Comput Appl 14(1):11–17CrossRef

30.

Cao L, Hong Y, Fang H, He G (1995) Predicting chaotic time series with wavelet networks. Physica D 85:225–238MATHCrossRef

31.

Chang PR, Weihui F, Minjun Y (1998) Short term load forecasting using wavelet networks. Eng Intell Syst Electr Eng Commun 6:217–230

32.

Khao TQD, Phuong LM, Binh PTT, Lien NTH (2004) Application of wavelet and neural network to long-term load forecasting. International Conference on Power System technology, POWERCON 2004, pp 840–844, Singapore

33.

Thuillard M (2000) Fuzzy logic in the wavelet framework. Proc Toolmet’2000, April 13–14, Oulu

34.

Thuillard M (2001) Wavelets in softcomputing. World Scientific Press, SingaporeCrossRef

35.

Lin CK, Wang SD (1996) Fuzzy modelling using wavelet transform. Electron Lett 32:2255–2256CrossRef

36.

Lin Y, Wang FY (2005) Predicting chaotic time series using adaptive wavelet-fuzzy inference system. In: Proceeding of IEEE intelligent vehicles symposium, Las Vegas, Nevada, USA, pp 888–893

37.

Guo QJ, Yu HB, Xu AD (2005) Wavelet fuzzy network for fault diagnosis. In: Proceedings of international conference on communications, circuits and systems. IEEE Press, pp 993–998

38.

Daniel WCH, Ping-An Z, Jinhua X (2001) Fuzzy wavelet networks for function learning. IEEE Trans Fuzzy Syst 9(1):200–211CrossRef

39.

Abiyev RH, Kaynak O (2008) Fuzzy wavelet neural networks for identification and control of dynamic plants—a novel structure and a comparative study. IEEE Trans Ind Electron 55(8):3133–3140CrossRef

40.

Abiyev RH (2005) Controller based of fuzzy wavelet neural network for control of technological processes CIMSA 2005. In: IEEE international conference on computational intelligence for measurement systems and applications, Giardini Naxos, Italy, pp 215–219

41.

Abiyev RH (2006) Time series prediction using fuzzy wavelet neural network model. Lecture Notes in Computer Sciences, Springer, Berlin, pp 191–200

42.

Abiyev RH (2009) Fuzzy wavelet neural network for prediction of electricity consumption. AIEDAM: Artif Intell Eng Des Anal Manuf 23(2):109–118

43.

Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New YorkMATH

44.

Chiu SL (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2:267–278MathSciNet

45.

Yager RR, Filev DP (1994) Generation of fuzzy rules by mountain clustering. J Intell Fuzzy Syst 2:267–278

46.

Demirli K, Muthukumaran P (2000) Higher order fuzzy system identification using subtractive clustering. J Intell Fuzzy Syst 9:129–158

47.

Kasabov NK (2002) DENFIS: Dynamic evolving neural-fuzzy inference system and its application for time-series. IEEE Trans Syst Fuzzy Syst 10(2):144–154CrossRef

48.

Wang LX (1997) A course in fuzzy systems and control. Prentice Hall, NJ, pp 183–189MATH

49.

Wang LX, Wei C (2000) Approximation accuracy of some neuro-fuzzy systems. IEEE Trans Fuzzy Syst 8(4):470–478CrossRef

50.

Juang C-F (2002) A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithm. IEEE Trans Fuzzy Syst 10:155–170CrossRef

51.

Juang CF, Lin CT (1998) An on-line self-constructing neural fuzzy inference network and its applications. IEEE Trans Fuzzy Syst 6(1):12–31CrossRef

52.

Zadeh LA (1975) The concept of linguistic variable and its application to approximate reasoning. Inf Sci 8

53.

Szu H, Telfer B, Garcia J (1996) Wavelet transforms and neural networks for compression and recognition. Neural Netw 9:695–708CrossRef

54.

Ku C-C, Lee KY (1995) Diagonal recurrent neural networks for dynamic systems control. IEEE Trans Neural Netw 6:144–156CrossRef

55.

Tong RM (1980) The evaluation of fuzzy models derived from experimental data. Fuzzy Sets Syst 4:1–12MATHCrossRef

56.

Pedtycz W (1984) An identification algorithm in fuzzy relational systems. Fuzzy Sets Syst 13:53–167

57.

Xu CW, Lu YZ (1987) Fuzzy model identification and self-learning for dynamic systems. IEEE Trans Syst Man Cybernet 17:683–689MATHCrossRef

58.

Sugeno M, Yasukawa T (1993) A fuzzy logic based approach to qualitative modelling. IEEE Trans Fuzzy Syst 1:7–31CrossRef

59.

Sugeno M, Tanaka K (1991) Successive identification of a fuzzy model and its application to prediction of complex system. Fuzzy Sets and Syst 42:315–334MATHCrossRefMathSciNet

60.

Lin Y, Cunningham GA III (1995) A new approach to fuzzy-neural system modelling. IEEE Trans Fuzzy Syst 3:190–198CrossRef

61.

Kim E, Park M, Ji S, Park M (1997) A new approach to fuzzy modelling. IEEE Trans Fuzzy Syst 5:328–337CrossRef

62.

Kim E, Park M, Kim S, Park M (1998) A transformed input-domain approach to fuzzy modelling. IEEE Trans Fuzzy Syst 6:596–604CrossRef

63.

Kim J, Kasabov NK (1999) HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems. Neural Netw 12:1301–1319CrossRef

Titel: Fuzzy wavelet neural network based on fuzzy clustering and gradient techniques for time series prediction
verfasst von: Rahib H. Abiyev
Publikationsdatum: 01.03.2011
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 2/2011
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-010-0414-4

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