PCA-RBPNN UNTUK KLASIFIKASI DATA MULTIVARIAT DENGAN ORTHOGONAL LEAST SQUARE (OLS) - Oni Soesanto (EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN, 2010)
This study will examine the PCA-RBPNN (Principal Component Analysis-Radial Basis) Probabilistic Neural Network) for the classification of multivariate data. The Main Component Analysis (PCA) has widely known in statistics as a method used to reduce the input dimension of the data multivariate by minimizing information loss. In this case, PCA is used to reduce dimensional input on the RBPNN neural network. The clustering process and initialization center is done with Self-Organizing Map (SOM). For the determination of weights during the learning process on the RBPNN network, using the Orthogonal Least Square (OLS) algorithm. Furthermore, PCA-RBPNN method is used for the classification of multivariate data. Accuracy of PCA-RBPNN classification is simulated and compared with the usual RBPNN model.