Comparison of Neural Models, Off-line and On-line Learning Algorithms for a Benchmark Problem

  • Authors:

  • António E. Ruano

  • Affiliations:

  • CST, FCT, University of Algarve, Faro, Portugal 8000

  • Venue:
  • IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
  • Year:
  • 2009

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Abstract

This paper compares the application of different neural models-multilayer perceptrons, radial basis functions and B-splines - for a benchmark problem, and illustrates the applicability of a common learning algorithm for all models considered. The learning algorithm is employed both for off-line training and for on-line model adaptation. In the latter case, a sliding window of past learning data is employed.