On Learning in a Time-Varying Environment by Using a Probabilistic Neural Network and the Recursive Least Squares Method

  • Maciej Jaworski
  • Marcin Gabryel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7267)

Abstract

This paper presents the recursive least squares method, combined with the general regression neural networks, applied to solve the problem of learning in time-varying environment. The general regression neural network is based on the orthogonal-type kernel functions. The appropriate algorithm is presented in a recursive form. Sufficient simulations confirm empirically the convergence of the algorithm.

Keywords

IEEE Transaction Mean Square Error Regression Function Probabilistic Neural Network General Regression Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Maciej Jaworski
    • 1
  • Marcin Gabryel
    • 1
  1. 1.Department of Computer EngineeringCzestochowa University of TechnologyCzestochowaPoland

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