Fundamentals and applications of fuzzy morphological associative memories

Orientador: Peter SussnerTese (doutorado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação CientificaResumo: Uma Memória Associativa (AM, Associative Memory) é um modelo projetado para armazenar pares de entrada e saída. Sobretudo, uma AM deve ser capaz de recordar uma sida desejada ao mesmo após a apresentação de uma versão incompleta ou destorcida de um padrão de entrada. Essa tese de doutorado discute as Memórias Associativas Morfológicas Nebulosas (FMAMs, Fuzzi Morphological Associative Memories), uma classe de memórias associativas elaboradas para armazenar padrões nebulosas cujos neurÔnios realizam operações elementares da morfologia matemática, i.e., dilatação, erosão, anti-dilatação e anti-erosão. É verificado que os principais modelos de Memória Associativa Nebulosa (FAM, Fuzzy Associative Memory) pertencem à classe das FMAMs. Essa tese introduz as Memórias Associativas Nebulosas Implicativas (IFAMs, Implicative Fuzzy Associative Memories) e suas versões duas com respeito à negação e adjunção. Uma IFAM é uma FMAM onde os pares de entrada e saída são armazenados usando o armazenamento nebuloso implicativo. No armazenamento nebuloso implicativo, os pesos sinápticos. Resultados sobre a fase de armazenamento faz IFAMs e das IFAMs duas são apresentados. Em particular, são demonstrados teoremas sobre a convergência, capacidade de armazenamento, tolerância à ruído e pontos fixos das IFAMs e das IFAMs duais para o caso autoassoplos e resultados teóricos. Finalmente, são apresentadas duas aplicações das FMAMs em problemas de previsão de séries temporais. O primeiro problema trata da previsão da mão-de-obra requerida em industrias metalúrgicas enquanto que a segunda aplicação refere-se a previsão da vazão média mensal da usina hidrelétrica de FurnasAbstract: Associative memories (AMs) are models that allow for the storage of pattern associations and the retrieval of the desired output pattern upon presentation of a possibly noisy or incomplete version of an input pattern. This thesis discusses fuzzy morphological associative memories (FMAMs), a general class of AMs designed to store fuzzy patterns and described by fuzzy neural networks. Each neuron of a FMAM model performs an elementary operation of mathematical morphology such as dilation, erosion, anti-dilation, and anti-erosion. We show that the most widely known models of fuzzy associative memories (FAMs) belong to the FMAM class. This thesis introduces the implicative fuzzy associative memories (IFAMs) and their dual versions with respect to negation and adjunction. An IFAM is a FMAM model where the patterns are stored by means of implicative fuzzy learning. Specifically, in implicative fuzzy learning, the synaptic weights are given by the minimum of the implication of pre- and postsynaptic activations. We present results concerning the recall and storing phase of IFAM and the dual IFAM models. In particular, we present theorems concerning the convergence, the storage capacity, the noise tolerance, and the fixed points of the IFAM and dual IFAM models in the auto-associative case. We compare the IFAMs with several others FAM models by means of theoretical results and examples. Finally, we present two applications of FMAM models in problems of time-series prediction. The first problem concerns the engineering manpower requirement in steel manufacturing industry while the second refers to the stream flow prediction of a large hydroelectric plant, namely FurnasDoutoradoDoutor em Matemática Aplicad

Predictive Fuzzy Clustering Model For Natural Streamflow Forecasting

Planning of hydroelectric systems is a complex and difficult task once it involves non-linear production characteristics and depends on numerous variables. A key variable is the natural streamflow. Streamflow values covering the entire planning period must be accurately forecasted because they strongly influence energy production. Currently, streamflow prediction using Box & Jenkins methodology prevails in the electric power industry. This paper suggests a fuzzy prediction model based on fuzzy clustering as an alternative for streamflow forecast. The model uses fuzzy c-means clustering to explore past data structure, and a median and pattern recognition procedures to capture similarities between streamflow history and data used for prediction. Computational experiments with actual data suggest that the predictive clustering approach performs globally better than periodic autoregressive moving average models, the current streamflow forecasting methodology adopted by many hydroelectric systems worldwide, and a fuzzy neural network, a non-linear prediction model.313491354Maier, H.R., Dandy, G., Neural networks for prediction and forecasting of water resources variables: A review of modelling issues and applications (2000) Environmental Modelling & Software, 15, pp. 101-124Box, G., Jenkins, G., Reinsel, G.C., (1994) Time Series Analysis, Forecasting and Control, 3rd Ed., , Oakland, California: Holden DayWeigend, A.S., Gershenfeld, N.A., (1993) Time Series Prediction: Prediction De Future and Understanding the Past, , Perseus PublishingBallini, 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. , Kluwer Academic Publishers: B. Bouchon- Meunier and R. R. Yager and L. Zadeh, EdsSee, L., Openshaw, S., A hybrid multi-model approach to river level forecasting (2000) Hydrology Science Journal, 45, pp. 523-536Chang, F.J., Chen, Y.C., A counterpropagation fuzzy neural network modeling approach to real time streamflow prediction (2001) Journal of Hydrology, 245, pp. 153-164Oh, 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 ScienceGeva, 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. 413-417Figueiredo, M., Gomide, F., Adaptive neuro fuzzy modelling (1997) Proceedings of FUZZ-IEEE'97, pp. 1567-1572Bezdek, J., (1981) Pattern Recognition with Fuzzy Objective Function Algorithms, , Plenum PressBallini, R., (2000) Streamflow Forecasting and Analysis Using Temporal Series, Neural Networks and Fuzzy Neural Networks (In Portuguese), , Ph.D. dissertation, State University of Campinas, Campinas, SP, Brazi

Streamflow Forecasting Using Neural Networks And Fuzzy Clustering Techniques

Planning of hydroelectric systems is a complex and difficult task once it involves non-linear production characteristics and depends on numerous variables. A key variable is the streamflow. Streamflow values covering the entire planning period must be accurately forecasted because they strongly influence energy production. This paper suggests an application of a FIR neural network and a fuzzy clustering-based model to evaluate one-step and multi-step ahead predictions. Results are compared to the ones obtained by a periodic autoregressive model (PAR). It is interesting to apply a recurrent neural network for prediction task due to its ability for temporal processing and efficiency to solve nonlinear problems. The results show a generally better performance of the FIR neural network for the case studied. © 2005 IEEE.426312636Maier, H., Dandy, G., Neutal networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications (2000) Environmental Modelling & Software, 15, pp. 101-124Box, G., Jenkins, G., Reinsel, G.C., (1994) Time Series Analysis, Forecasting and Control, 3rd Ed., , Oakland, California, EUA: Holden DayWeigend, A.S., Gershenfeld, N.A., (1992) Time Series Prediction: Forecasting the Future and Understanding the PastSlim, C., Trabelsi, A., Neural network for modeling financial time series: A new approach (2003) ICCSA, (3), pp. 236-245Lapedes, A., Farber, R., Nonlinear signal processing using neural networks: Prediction and system modelling (1987) Tech. Rep. LA-UR-&-2662, , Los Alamos National LaboratoryKim, H.J., Lee, W.D., Yang, H.S., A modified FIR network for time series prediction (2002) Proceedings of the 9th International Conference on Neural Information Processing, 5, pp. 2597-2600Ku, C., Lee, K., Diagonal recurrent neural networks for dynamic systems control (1995) IEEE Transactions on Neural Networks, 6 (1), pp. 144-156Oh, K., 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 ScienceGeva, A., Non-stationary time series prediction using fuzzy clustering (1999) Proceedings of the International Conference of the North American Fuzzy Information Processing Society, pp. 413-417Wan, E., Temporal backpropagation for FIR neural networks (1990) International Joint Conference on Neural Networks, 1, pp. 575-580. , JuneMagalhães, M., Ballini, R., Gonçalves, R., Gomide, F., Predictive fuzzy clustering model for natural streamflow forecasting (2004) Proceedings of the IEEE International Conference on Fuzzy Systems, pp. 390-394. , Budapest, Hungary, JulyHaykin, S., (2001) Neural Networks: A Comprehensive Foundation, 2nd Ed., , Prentice Hall, IncWang, L.-F., Li, X.-X., Model identification of time delay nonlinear system with FIR neural network (2003) Proceedings of the Second International Conference on Machine Learning and Cybernetics, 2, pp. 872-875. , NovemberBezdek, J., (1981) Pattern Recognition with Fuzzy Objective Function Algorithms, , New York, EUA: Plenum PressSchwarz, G., Estimating the dimension of a model (1978) The Annual of Statistics, 6 (2), pp. 461-46

Combining Forecasts For Natural Streamflow Prediction

This paper proposes an approach to combine forecasts generated by a set of individual forecasting models in a simple and effective way. In principle, combination can be done using appropriate aggregation operators, but here we use a neural network trained with the gradient algorithm. The aim is to combine the forecasts generated by the different forecasting models as an attempt to capture the contributions of the most Important prediction features of each individual model at each prediction step. The approach is used for streamflow time series prediction choosing, as individual forecasting models, periodic autoregressive moving average model (PARMA), and two fuzzy clustering-based forecasting models. Experimental results with actual streamflow data show that the combination approach performs better than each of the individual forecasting models and yet, when compared to a fuzzy neural network (FNN) evaluation, the suggested combination model shows lower prediction errors.1390394Box, G., Jenkins, G., Reinsel, G.C., (1994) Time Series Analysis, Forecasting and Control, 3rd Ed., , Oakland, California: Holden DayWeigend, A.S., Gershenfeld, N., (1993) Time Series Prediction: Prediction de Future and Understanding the Past, , Perseus PublishingPedrycz, W., Gomide, F., (1998) An Introduction to Fuzzy Sets: Analysis and Design, , Cambridge, MA: MIT PressBallini, 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. , Kluwer Academic Publishers: B. Bouchon- Meunier and R. R. Yager and L. Zadeh, EdsSee, L., Openshaw, S., A hybrid multi-model approach to river level forecasting (2000) Hydrology Science Journal, (45), pp. 523-536Chang, F., Chen, Y., A counterpropagation fuzzy neural network modeling approach to real time streamflow prediction (2001) Journal of Hydrology, (245), pp. 153-164Geva, A., Non-stationary time series prediction using fuzzy clustering (1999) Proceedings of the 18th International Conference of the North American Fuzzy Information Processing Society, pp. 413-417Armstrong, J., Combining forecasts: The end of the beginning or the beginning of the end? (1989) International Journal of Forecasting, 5 (4), pp. 585-588Makridakis, S., Why combining works? (1989) International Journal of Forecasting, 5 (4), pp. 601-603Clemen, R., Combining forecasts: A review and annotated bibliography (1989) International Journal of Forecasting, 5 (4), pp. 559-583Sharkey, A., (1999) Combining Artificial Neural Nets: Ensemble and Modular Multi-net Systems, , SpringerPetridis, V., Kehagias, A., (1998) Predictive Modular Neural Networks, , Kluwer Academic PublishersVecchia, A., Maximum likelihood estimation for periodic autoregressive moving average models (1985) Technometrics, 27 (4), pp. 375-384Magalhães, M., Ballini, R., Gomide, F., Predictive fuzzy clustering model for natural streamflow forecasting (2004) Proceedings of FUZZY-IEEE'04, , Budapest, Hungary, July (submitted)Bezdek, J., (1981) Pattern Recognition with Fuzzy Objective Function Algorithms, , Plenum PressFigueiredo, M., Gomide, F., Adaptive neuro fuzzy modelling (1997) Proceedings of FUZZ-IEEE'97, pp. 1567-1572Anderson, P.L., Vecchia, A.V., Asymptotic results for periodic autoregressive moving average processes (1993) Journal Time Series Anal, 1, pp. 1-18Schwarz, G., Estimating the dimension of a model (1978) Ann. Statist., 6 (2), pp. 461-468Haykin, S., (1994) Neural Networks - A Comprehensive Foundation, , New York: IEEE PressFigueiredo, M., Ballini, R., Soares, S., Andrade, M., Gomide, F., Learning algorithms for a class of neurofuzzy network and application (2004) IEEE Transactions on Man System and Cybernetics, Part C, , in pres