Abstract
In large number of real world dilemmas and applications, especially in industrial areas, efficient processing of the data is a chief condition to solve problems. The constraints relative to the nature of data to be processed, difficult dilemma related to the choice of appropriated processing techniques and allied parameters make complexity reduction a key point on both data and processing levels. In this paper we present an ANN based data driven treelike Multiple Model generator, that we called T-DTS (Treelike Divide To Simplify), able to reduce complexity on both data and processing levels. The efficiency of such approach has been analyzed trough applications dealing with none-linear process identification. Experimental results validating our approach are reported and discussed.
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References
Multiple Model Approaches to Modeling and Control, edited by R. Murray-Smith and T.A. Johansen, Taylor & Francis Publishers, 1997, ISBN 0-7484-0595-X.
Goonatilake S. and Khebbal S.: Issues, Classification and Future Directions. In Intelligent Hybrid Systems. John Wiley & Sons, pp 1–20, ISBN 0 471 94242 1.
Krogh A., Vedelsby J.: Neural Network Ensembles, Cross Validation, and Active Learning, in Advances in Neural Information Processing Systems 7, The MIT Press, Ed by G. Tesauro, pp 231–238, 1995.
Sridhar D.V., Bartlett E.B., Seagrave R.C., “An information theoretic approach for combining neural network process models”, Neural Networks, Vol. 12, pp 915–926, Pergamon, El-sevier, 1999.
Jordan M. I. and Xu L., “Convergence Results for the EM Approach to Mixture of Experts Architectures”, Neural Networks, Vol. 8, N° 9, pp 1409–1431, Pergamon, Elsevier, 1995.
Bruske J., Sommer G., Dynamic Cell Structure, Advances, in Neural Information Processing Systems 7, The MIT Press, Ed by G. Tesauro, pp 497–504, 1995.
Sang K. K. and Niyogi P., Active learning for function approximation, in Neural Information Processing Systems 7, The MIT Press, Ed by G. Tesauro, pp 497–504.
Madani K., Chebira A., “A Data Analysis Approach Based on a Neural Networks Data Sets Decomposition and it’s Hardware Implementation”, PKDD 2000, Lyon, France, 2000.
Jollifee I.T., “Principle Component Analysis”, New York, Springer Verlag 1986.
Kohonen T., “Self-Organization and Associative Memory”, Springer-Verlag, 1984.
Lang K. J. and Witbrock M. J., Learning to tell two spirals apart. Proc. of the 1988 Connectionist Models Summer School, Morgan Kauffrnan, pp 52–59, (1988).
Desmartines P., Hérault J., “Representation of Non Linear Data Structures Trought a Fast VQP Neural Networks”, Neuro-Nimes’93 Proceedings, pp 411–424, Nîmes, France, 1993.
Reyneri L., “Weighted Radial Basis Function for Improved Pattern Recognition and Signal Processing’, from Neural Processing Letters, Vol. 2, N°3, pp 2–6, 1995.
Rumelhart D., Hinton G., Williams R., “Learning Internal Representations by Error Propagation”, “Parallel Distributed Processing ”, I & II, MIT Press, Cambridge MA, 1986.
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Madani, K., Chebira, A., Rybnik, M. (2003). Data Driven Multiple Neural Network Models Generator Based on a Tree-like Scheduler. In: Mira, J., Álvarez, J.R. (eds) Computational Methods in Neural Modeling. IWANN 2003. Lecture Notes in Computer Science, vol 2686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44868-3_49
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DOI: https://doi.org/10.1007/3-540-44868-3_49
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