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A neural network classifier in experimental particle physics

A A Handzel1, T Grossman, E Domany

  • 1Department of Electronics, Weizmann Institute of Science, Rehovot, Israel.

International Journal of Neural Systems
|June 1, 1993
PubMed
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A new method using the CHIR algorithm trains binary neural networks for high energy physics classification. This approach overcomes training instability and matches backpropagation performance.

Area of Science:

  • High Energy Physics
  • Computational Neuroscience
  • Machine Learning

Background:

  • Classification problems in high energy physics present significant computational challenges.
  • Traditional neural network training can be unstable, especially with nonseparable datasets.

Purpose of the Study:

  • To solve a high energy physics classification problem using a novel approach.
  • To overcome training instability in multilayer perceptrons with binary units.

Main Methods:

  • A simple multilayer perceptron with binary units was trained using the CHIR algorithm.
  • Weight vectors were selected for optimal performance and flexible error classification.
  • The CHIR algorithm was adapted for continuous input and integrated with phi-machine concepts.

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Main Results:

  • The CHIR algorithm successfully trained the binary neural network on simulated high energy physics data.
  • The method demonstrated stable training and flexible control over classification errors.
  • Performance was comparable to the widely used backpropagation algorithm.

Conclusions:

  • The CHIR algorithm offers a viable and effective alternative for training binary neural networks in high energy physics.
  • This work highlights the potential of adapting novel training algorithms and incorporating established computational models into modern neural network architectures.