JETNET 3.0—A versatile artificial neural network package

Abstract

For a problem that is encoded with ni input nodes, no output (feature) nodes, H layers of hidden OCR Output The only restriction of the complexity for an application is set by available memory and CPU time. Restriction of complexity of the problem can be used for any pattern recognition problem area. used with success for heavy quark tagging and quark-gluon separation, it is of general nature and package was originally mainly intended for jet triggering applications [2, 3, 4], where it has been must be loaded with a main application specific program supplied by the user. Even though the 3. 0 package consists of a number of subroutines, most of which handle training and test data, that is pointed out. The self-organizing part is unchanged and is hence not described here. The JETNET this manual and the relation between the underlying algorithms and standard statistical methods networks. A set of rules-of-thumb on when, why and how to use the various options is presented in versions and contains a number of powerful elaborate options for updating and analyzing MLP map algorithm as well. The present version, JETNET 3.0, is backwards compatible with older versions of such networks using the back-propagation updating rule, and included a self-organizing their simplicity and excellent performance. The F77 package J ETNET 2 .0 [1] implemented "vanilla" methods. In particular feed-forward multilayer perceptron (MLP) networks are widely used due to Artificial Neural Networks (ANN) constitute powerful nonlinear extensions of the conventional Method of solution Fischer discriminants, principal components analysis and ARMA models. control. Standard methods for such problems are typically confined to linear dependencies like ing from off-line and on-line parton (or other constituent) identification tasks to accelerator beam Challenging pattern recognition and non-linear modeling problems within high energy physics, rang Nature of physical problem Keywords: pattern recognition, jet identification, data analysis, artificial neural network CPC Library subroutines used: none No. of lines in combined program and test deck: 5753 Peripherals used: terminal for input, terminal or printer for output No. of bits in a word: 32 High speed storage required: M 90k words Program language used: FORTRAN 77 • Langevin Updating [6] OCR Output • Standard Gradient Descent (back-propagation) [5] The following learning algorithms are included in JETNET 3. O: performance and estimating error surfaces. cern additional learning algorithm variants, learning parameters and various tools for gauging [1, 4] for information on this part. For the MLP the most important additions and changes con the self-organizing networks nothing is changed in JETNET 3.0 and we refer the reader to refs. dating and self-organizing networks. Both these approaches were implemented in JETNET 2 .0. For used architectures and procedures are the Multilayer Perceptron (MLP) with backpropagation up is no exception with its demanding on-line and off-line analysis tasks. To date, the ...

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