Dynamic perceptron: some theorems about the possibility of parallel pattern recognition with an application to high-energy physics

Antonio Luigi Perrone; Patrizia Castiglione; Gianfranco Basti; Roberto Messi

doi:10.1117/12.170003

2 March 1994 Dynamic perceptron: some theorems about the possibility of parallel pattern recognition with an application to high-energy physics

Antonio Luigi Perrone, Patrizia Castiglione, Gianfranco Basti, Roberto Messi

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Proceedings Volume 2243, Applications of Artificial Neural Networks V; (1994) https://doi.org/10.1117/12.170003
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

Abstract

In the context of M. Minsky's and S. Papert's theorems on the impossibility of evaluating simple linear predicates by parallel architectures we want to show how these limitations can be avoided by introducing a generalized input-dependent preprocessing technique that does not suppose any a-priori knowledge of input like in classical input filtering procedures. This technique can be formalized in a very general way and can be also deduced by meta- mathematical arguments. A further development of the same technique can be applied at level of learning procedure to introduce in such a way the complete notion of `dynamic perception'. From the experimental standpoint, we show two applications of the dynamic perceptron in particle track recognition in high-energy accelerators. Firstly, we show the amazing improvement of performances that can be obtained in a perceptron architecture with classical learning by adding our dynamic preprocessing technique, already introduced last year in another paper presented at this Conference. Secondly, we show the first results of this technique extended also at the level of learning procedure always applied to the problem of particle track recognition.

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Antonio Luigi Perrone, Patrizia Castiglione, Gianfranco Basti, and Roberto Messi "Dynamic perceptron: some theorems about the possibility of parallel pattern recognition with an application to high-energy physics", Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); https://doi.org/10.1117/12.170003

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Physics

Retina

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