Multiple multivariate regression and global optimization in a large scale thermodynamical application

  • H. Zaragoza
  • P. Gallinari
Part V: Robotics, Adaptive Autonomous Agents, and Control
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1327)


We describe a large scale real-world application of neural networks for the modelization of heat radiation emitted by a source and observed through the atmosphere. For this problem, thousands of regressors need to be trained and incorporated into a single model of the process. On such large scale applications, standard techniques for the control of complexity are impossible to implement. We investigate the interest of i) integrating several regressors into a single neural network, and ii) refining the learned functions by optimizing simultaneously all regressors over a global function. The two approaches described offer a solution to these problems, and were crucial for the development of a fast and accurate model of radiation intensity.


Ordinary Little Square Parameter Function Hide Unit Spectral Transmissivity Single Neural Network 
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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • H. Zaragoza
    • 1
  • P. Gallinari
    • 1
  1. 1.LIP6, Université Pierre et Marie CurieParis cedex 05France

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