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[Illinois]: Error Gradient Estimations Due to Parallel Perturbation of Weights

By AbderRahman N Sobh1, Jessica S Johnson1, NanoBio Node1

1. University of Illinois at Urbana-Champaign

This tool trains two-layered networks of sigmoidal units to associate patterns using simultaneous perturbation of weights.

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Version 1.0b - published on 06 Aug 2014

doi:10.4231/D3SQ8QJ4B cite this

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Abstract

From Tutorial on Neural Systems Modeling, Chapter 7:

This tool trains two-layered, feedforward networks of sigmoidal units on pattern association tasks by estimating the network error gradient using parallel weight pertur­bation, and by updating all network weights simultaneously. This is similar to the one-weight-at-a-time tool pertGradient1By1, except that all weights are perturbed and updated simultaneously. Also the network is not limited to one output unit only.

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NanoBio Node, University of Illinois Champaign-Urbana

Cite this work

Researchers should cite this work as follows:

  • Tutorial on Neural Systems Modeling, Copyright 2010 Sinauer Associates Inc. Author: Thomas J. Anastasio
  • AbderRahman N Sobh; Jessica S Johnson; NanoBio Node (2014), "[Illinois]: Error Gradient Estimations Due to Parallel Perturbation of Weights," http://nanohub.org/resources/pertgradll. (DOI: 10.4231/D3SQ8QJ4B).

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