Capítulos de libros
Granular Models For Time-series Forecasting
Registro en:
9780470035542
Handbook Of Granular Computing. John Wiley & Sons, Ltd, v. , n. , p. 949 - 967, 2008.
10.1002/9780470724163.ch45
2-s2.0-79953681978
Autor
Magalhaes M.H.
Ballini R.
Gomide F.A.C.
Institución
Resumen
[No abstract available]
949 967 Box, G.E.P., Jenkins, G.M., Reinsel, G.C., Time Series Analysis, Forecasting and Control (1994), 3rd ed. Holden Day, Oakland, CAWeigend, A.S., Gershenfeld, N.A., Time Series Prediction: Forecasting de Future and Understanding the Past (1994), Addison-Wesley, Santa FE, NMMaier, H.R., Dandy, G.C., Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications (2000) Environ. Modelling Softw, 15, pp. 101-124 Ballini, R., Figueiredo, M., Soares, S., Andrade, M., Gomide, F., A seasonal streamflow forecasting model using neurofuzzy network (2000) Information, Uncertainty and Fusion, pp. 257-276. , In: B. Bouchon-Meunier, R.R. Yager and L. Zadeh (eds),, 1st ed. Kluwer Academic Publishers, Norwell, MA See, L., Openshaw, S., A hybrid multi-model approach to river level forecasting (2000) Hydrol. Sei. J, 45, pp. 523-536 Chang, F.J., Chen, Y.C., A counterpropagation fuzzy neural network modeling approach to real time streamflow prediction (2001) J. Hydrol, 245, pp. 153-164 Figueiredo, M., Ballini, R., Soares, S., Andrade, M., Gomide, F., Learning algorithms for a class of neurofuzzy network and application (2004) IEEE Trans. Syst. Man Cybern, 34 (3), pp. 293-301 Magalhäes, M.H., Ballini, R., Gongalves, R., Gomide, F., Predictive fuzzy clustering model for natural stream-flow forecasting (2004) Proceedings of the IEEE International Conference on Fuzzy Systems, pp. 390-394. , In:, Budapest, Hungary, July Takagi, T., Sugeno, M., Fuzzy identification of systems and its applications to modeling and control (1985) IEEE Trans. Syst. Man Cybern, 15, pp. 116-132 Setnes, M., Babuska, R., Verbraggen, H., Rule-based modeling: precision and transparency (1998) IEEE Trans. Syst. Man Cybern, 28 (1), pp. 165-169 Yen, J., Wang, L., Gillespie, andC., Inproving the interpretability of tskfuzzy models by combining global learning and local learning (1998) IEEE Trans. Fuzzy Syst, 6 (4), pp. 530-537 Oh, K.J., Han, I., An intelligent clustering forecasting system based on change-point detection and artificial neural networks: Application to financial economics (2001) Proceedings of the 34th Hawaii International Conference on System Science, , In:, Maui, Hawaii, January Bezdek, J., Pattern Recognition with Fuzzy Objective Function Algorithms (1981), Kluwer Academic Publishers, Norwell, MAPedrycz, W., Vasilakos, A.V., Linguistic models and linguistic modeling (1999) IEEE Trans. Syst. Man Cybern, 29 (6), pp. 745-757 Pedrycz, W., Bargiela, A., Granular clustering: A granular signature of data (2002) IEEE Trans. Syst. Man Cybern, 32 (2), pp. 212-224 Bargiela, A., Pedrycz, W., Granulation of temporal data: A global view on time series (2003) Fuzzy Information Processing Society. 22nd International Conference of the North American-NAFIPS, pp. 191-196. , In:, Chicago, IL, July Bargiela, A., Pedrycz, W., Recursive information granulation: aggregation and interpretation issues (2003) IEEE Trans. Syst. Man Cybern, 33 (1), pp. 96-112 Sayal, M., Shan, M.-C., Analysis of numeric data streams at different granularities (2005) IEEE Int. Conf. Granular Comput, 1, pp. 237-242 Bargiela, A., Granular modeling through regression analysis (2006) Proceedings of Information Processing and Management of Uncertainty in Knowledge-Based Systems-IPMU, 1, pp. 1474-1480. , In:, Paris, France, July Bargiela, A., Pedrycz, W., Granular Computing: An Introduction (2003), Kluwer Academic Publisher, Norwell, MAGeva, A.B., Non-stationary time series prediction using fuzzy clustering (1999) Proceedings of the 18th International Conference of the North American Fuzzy Information Processing Society, pp. 413-417. , In:, New York, June Geva, A.B., Hierarchical-fuzzy clustering of temporal-patterns and its applications for time-series prediction (1999) Pattern Recognit. Lett, 20 (14), pp. 1599-1532 Figueiredo, M., Gomide, F., Adaptive neuro fuzzy modelling (1997) Proceedings of FUZZ-IEEE, pp. 1567-1572. , In:, Barcelona, Spain, July Figueiredo, M., Gomide, F., Design of fuzzy systems using neurofuzzy networks (1999) IEEE Trans. Neural Netw, 10 (4), pp. 815-827 Pedrycz, W., Knowledge-Based Clustering From Data to Information Granules (2005), John Wiley & Sons, Roboken, NJGeva, A.B., Feature extraction and state recognition in biomedical signals with hierarchical unsupervised fuzzy clustering methods (1998) Med. Biol. Eng. Comput, 36 (5), pp. 608-614 Vecchia, A.V., Maximum likelihood estimation for periodic autoregressive moving average models (1985) Technomet-rics, 27 (4), pp. 375-384