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A new adaptive polynomial neural network

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  • Balestrino, A.
  • Bini Verona, F.

Abstract

This paper considers the problem of the construction of nonlinear mapping by using an adaptive polynomial neural network [1], implementing a learning rule. First we apply the method to a two-class pattern recognition problem. We use one high order neuron with a threshold element ranging from −1 to +1. Positive output means class 1 and negative output means class 2. The main idea of the method proposed is that the weights are adjusted automatically in such a way to move the decision boundary to a place of low pattern density. Once reached the convergence, to improve the generalization ability, we add a growing noise to the data available. The training is performed by a steepest-descent algorithm on the weights.

Suggested Citation

  • Balestrino, A. & Bini Verona, F., 1994. "A new adaptive polynomial neural network," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 37(2), pages 189-194.
    • Handle: RePEc:eee:matcom:v:37:y:1994:i:2:p:189-194
    DOI: 10.1016/0378-4754(94)90017-5
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