Neural networks for control
In recent years there has been increasing interest in universal model-free controllers. Mimicing the functions of human processes, these controllers learn about the systems they are controlling on-line, and thus automatically improve their performance. So far, neural networks have made their mark in the areas of classification and pattern recognition; with this success they've become an important tool in the repertoire of the signal processor and computer scientist. However, the same cannot be said for neural networks in system theory applications.
There has been a good deal of research on the use of neural networks for control, although most of the articles have been ad hoc discussions lacking theoretical proofs and repeatable design algorithms. As a result, neither the control systems community nor US industry have fully accepted neural networks for closed-loop control applications. In this article, I address the major problems facing neural network control
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2013 LOUIS-JEANTET Prize for Medecine — Cordis News
The 2013 LOUIS-JEANTET PRIZE FOR MEDICINE is awarded to the geneticist, Michael Stratton, director of the Wellcome Trust Sanger Institute in Cambridge, and jointly to the German biochemists Peter Hegemann, researcher at the Department of Experimental ...