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A new adaptive polynomial neural network

✍ Scribed by A. Balestrino; F. Bini Verona

Publisher: Elsevier Science
Year: 1994
Tongue: English
Weight: 404 KB
Volume: 37
Category: Article
ISSN: 0378-4754
DOI: 10.1016/0378-4754(94)90017-5

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✦ Synopsis

This paper considers the problem of the construction of nonlinear mapping by using an adaptive polynomial neural network [l], 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.

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