Principles of computational dynamics: applications to parallel and neural computations

Antonio Luigi Perrone; Enrico Pasqualucci; Roberto Messi; Gianfranco Basti; Piergiorgio Picozza; Walter Pecorella; Luciano Paoluzi

doi:10.1117/12.235973

22 March 1996 Principles of computational dynamics: applications to parallel and neural computations

Antonio Luigi Perrone, Enrico Pasqualucci, Roberto Messi, Gianfranco Basti, Piergiorgio Picozza, Walter Pecorella, Luciano Paoluzi

Author Affiliations +

Proceedings Volume 2760, Applications and Science of Artificial Neural Networks II; (1996) https://doi.org/10.1117/12.235973
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

Abstract

In this paper, starting from a general discussion on neural network dynamics from the standpoint of statistical mechanics, we discuss three different strategies to deal with the problem of pattern recognition in neural nets. Particularly we emphasized the role of matching the intrinsic correlations within the input patterns, to solve the problem of the optimal pattern recognition. In this context, the first two strategies, we applied to different problems and we discuss in this paper, consist essentially in adding either white noise or colored noise (deterministic chaos) on the input pattern pre-processing, to make easier for a classical backpropagation algorithm the class separation, respectively because the input patterns are too correlated among themselves or, on the contrary, are too noisy. The third more radical strategy, we applied to very hard pattern recognition problems in HEP experiments, consists in an automatic (dynamic) redefinition of the same net topology on the inner correlations of the inputs.

Citation Download Citation

Antonio Luigi Perrone, Enrico Pasqualucci, Roberto Messi, Gianfranco Basti, Piergiorgio Picozza, Walter Pecorella, and Luciano Paoluzi "Principles of computational dynamics: applications to parallel and neural computations", Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); https://doi.org/10.1117/12.235973

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available