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Multi-modeling: A Different Way to Design Intelligent Predictors

Abstract : Recently, multiple works proposed multi-model based approaches to model nonlinear systems. Such approaches could also be seen as some “specific” approach, inspired from ANN operation mode, where each neuron, represented by one of the local models, realizes some higher level transfer function. We are involved in nonlinear dynamic systems identification and nonlinear dynamic behavior prediction, which are key steps in several areas of industrial applications. In this paper, two identifiers architectures issued from the multimodel concept are presented, in the frame of nonlinear system's behavior prediction context. The first one, based on “equation error” identifier, performs a prediction based on system's inputs and outputs. However, if the system's inputs are often accessible, its outputs are not always available in prediction phase. The second one, called “output error” based identifier/predictor needs only the system's inputs to achieve the prediction task. Experimental results validating presented multi-model based structures have been reported and discussed.
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Contributor : Rachid Malti <>
Submitted on : Wednesday, July 29, 2009 - 11:28:29 AM
Last modification on : Tuesday, March 31, 2020 - 2:12:13 PM


  • HAL Id : hal-00408149, version 1


Lamine Thiaw, Kurosh Madani, Rachid Malti, Gustave Sow. Multi-modeling: A Different Way to Design Intelligent Predictors. J. Cabestany, A. Prieto, and D.F. Sandoval. Lecture Notes in Computer Science, Computational Intelligence and Bioinspired Systems, Springer, ISBN 3-540-26208-3, pp.976-984, 2005. ⟨hal-00408149⟩



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