By a News Reporter-Staff News Editor at Journal of Technology -- Current study results on Perceptron have been published. According to news reporting originating from Toulouse, France, by VerticalNews correspondents, research stated, "Thermal engineering deals with the estimation of the temperature at different spatial points and different instants for a given set of boundary and initial conditions. For this purpose, the reference model is a numerical simulation model but it is time-consuming."
Our news editors obtained a quote from the research from the Institute of Mathematics, "Consequently we build a surrogate model in order to replace it. This surrogate model is a recursive multilayer perceptron, independent of the boundary conditions and parametrized by the statistical learning of multidimensional temporal trajectories computed with the reference model. It emulates the outputs of the reference model over time from the only knowledge of initial conditions and exogenous variables. Moreover this model is able to predict these outputs in steady state, even if its formulation is time-dependent. A new methodology is proposed so as to overcome the learning problem associated to the very weak number of trajectories available for the surrogate model construction. The first step attempts to build a more robust surrogate model by considering it as the average of local models resulting from the V-folds cross-validation technique. This new kind of multilayer perceptron is much more robust and accurate, in particular when the learning dataset is very small. The second step consists in the creation of a new learning dataset which is made up of each time observation coming from each trajectory. In this way, we artificially obtain a sizeable sample allowing all the classic neural networks constructions. Furthermore, many approaches exist in order to select the best hidden neurons number but most of them are costly or require a lot of observations. We consider here a non-asymptotic approach based on the minimization of a penalized criterion providing accurate results in an economical computational way. In order to calibrate precisely the penalty term, we use the slope heuristic or the dimension jump, recently introduced in a regression framework. The validation of the method is performed on a toy function."
According to the news editors, the research concluded: "The prediction ability of the surrogate model built with the new methodology is successfully compared to usual constructions on a simplified problem and then applied to thermal engineering."
For more information on this research see: Multi layer perceptron for the learning of spatio-temporal dynamics-application in thermal engineering. Engineering Applications of Artificial Intelligence, 2013;26(10):2270-2286. Engineering Applications of Artificial Intelligence can be contacted at: Pergamon-Elsevier Science Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England. (Elsevier - www.elsevier.com; Engineering Applications of Artificial Intelligence - www.elsevier.com/wps/product/cws_home/975)
The news editors report that additional information may be obtained by contacting M. De Lozzo, Inst Math Toulouse INSA Toulouse, F-31077 Toulouse 4, France. Additional authors for this research include P. Klotz and B. Laurent.
Keywords for this news article include: France, Europe, Toulouse, Perceptron, Machine Learning, Thermal Engineering, Emerging Technologies
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