Abstract
This paper presents a parallel implementation of a hybrid data mining technique for multivariate heterogeneous time varying processes based on a combination of neuro-fuzzy techniques and genetic algorithms. The purpose is to discover patterns of dependency in general multivariate time-varying systems, and to construct a suitable representation for the function expressing those dependencies. The patterns of dependency are represented by multivariate, non-linear, autoregressive models. Given a set of time series, the models relate future values of one target series with past values of all such series, including itself. The model space is explored with a genetic algorithm, whereas the functional approximation is constructed with a similarity based neuro-fuzzy heterogeneous network. This approach allows rapid prototyping of interesting interdependencies, especially in poorly known complex multivariate processes. This method contains a high degree of parallelism at different levels of granularity, which can be exploited when designing distributed implementations, such as workcrew computation in a master-slave paradigm. In the present paper, a first implementation at the highest granularity level is presented. The implementation was tested for performance and portability in different homogeneous and heterogeneous Beowulf clusters with satisfactory results. An application example with a known time series problem is presented.
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References
Belanche, Ll.: Heterogeneous neural networks: Theory and applications. PhD Thesis, Department of Languages and Informatic Systems, Polytechnic University of Catalonia, Barcelona, Spain, July, (2000)
Birx, D., Pipenberg, S.: Chaotic oscillators and complex mapping feedforward networks for signal detection in noisy environment. Int. Joint Conf. On Neural Networks (1992)
Box, G., Jenkins, G.: Time Series Analysis, Forecasting and Control. Holden-Day. (1976)
Chandon, J.L., Pinson, S.: Analyse Typologique. Théorie et Applications. Masson, Paris, (1981)
Gueist, A., et.al.: PVM. Parallel Virtual Machine. Users Guide and Tutorial for Networked Parallel Computing. MIT Press 02142, (1994)
Lapedes, A., Farber, R.: Nonlinear signal processing using neural networks: prediction and system modeling. Tech. Rep. LA-UR-87-2662, Los Alamos National Laboratory, NM, (1987)
Masters, T.: Neural, Novel & Hybrid Algorithms for Time Series Prediction. John Wiley & Sons, (1995)
Specht, D.: Probabilistic Neural Networks, Neural Networks 3. (1990), 109–118
Valdés, J.J., García, R.: A model for heterogeneous neurons and its use in configuring neural networks for classification problems. Proc. IWANN’97, Int. Conf. On Artificial and Natural Neural Networks. Lecture Notes in Computer Science 1240, Springer Verlag, (1997), 237–246
Valdés, J.J., Belanche, Ll., Alquézar, R.: Fuzzy heterogeneous neurons for imprecise classification problems. Int. Jour. Of Intelligent Systems, 15(3), (2000), 265–276.
Valdés, J.J.: Similarity-based Neuro-Fuzzy Networks and Genetic Algorithms in Time Series Models Discovery. NRC/ERB-1093, 9 pp. NRC 44919. (2002)
Wall, T.: GaLib: A C++ Library of Genetic Algorith Components. Mechanical Engineering Dept. MIT (http://lancet.mit.edu/ga/), (1996)
Zadeh, L.: The role of soft computing and fuzzy logic in the conception, design and deployment of intelligent systems. Proc. Sixth Int IEEE Int. Conf. On Fuzzy Systems, Barcelona, July 1–5, (1997)
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Valdés, J.J., Mateescu, G. (2002). Time Series Model Mining with Similarity-Based Neuro-fuzzy Networks and Genetic Algorithms: A Parallel Implementation. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds) Rough Sets and Current Trends in Computing. RSCTC 2002. Lecture Notes in Computer Science(), vol 2475. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45813-1_36
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DOI: https://doi.org/10.1007/3-540-45813-1_36
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