Performance Improvement of General Regression Neural Network Using Principal Component Analysis 


Vol. 7,  No. 11, pp. 3408-3416, Nov.  2000
10.3745/KIPSTE.2000.7.11.3408


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  Abstract

This paper proposes an efficient method for improving the performance of a general regression neural network by using the feature to the independent variables as the center for pattern-layer neurons. The adaptive principal component analysis is applied for extracting efficiently the features by reducing the dimension of the given independent variables. It can achieve a superior property of the principal component analysis that converts input data into set of statistically independent features and the general regression neural network, respectively. The proposed general regression neural network has been applied to regress the Solow''s economy (2-independent variable set) and the wire telephone(4-independent variable set). The simulation results show that the proposed neural networks have better performances of the regression for the test data, in comparison with those using the means or the weighted means of independent variables. Also, it is affected less by the number of neurons and the scope of the smoothing factor.

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[IEEE Style]

Y. H. Cho, "Performance Improvement of General Regression Neural Network Using Principal Component Analysis," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 7, no. 11, pp. 3408-3416, 2000. DOI: 10.3745/KIPSTE.2000.7.11.3408.

[ACM Style]

Yong Hyun Cho. 2000. Performance Improvement of General Regression Neural Network Using Principal Component Analysis. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 7, 11, (2000), 3408-3416. DOI: 10.3745/KIPSTE.2000.7.11.3408.

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