Evolutionary based smoothing parameter optimization of probabilistic neural network and its usage as a classifier in odor recognition system

Authors:
Herry Benyamin Kusumoputro
Affiliations:
Faculty of Computer Science, University of Indonesia
Venue:
ACMOS'05 Proceedings of the 7th WSEAS international conference on Automatic control, modeling and simulation
Year:
2005

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Abstract

Probabilistic Neural Network has received considerable attention nowadays and obtained many successful application. This type of neural system has shown marvelous higher recognition capability compare with that of Back-Propagation neural system. However, this neural has shown some drawbacks, especially on determining the value of its smoothing parameter and its neural structure optimization when large number of data is necessary. Supervised-structure determination of PNN is an algorithm to solve these problems by selecting a set of valuable neurons using Orthogonal Algorithm and determining the optimal smoothing parameter value using Genetic Algorithm. In this paper an experimental set up for comparison of the Supervised-structure determination of PNN with that of the Standard PNN as a neural classifier on the Odor Recognition System is conducted. Experimental results show that the Supervised-structure determination of PNN performed higher recognition rate compare with that of Standard PNN, even using lower number of neurons.