IJCRR - 6(16), August, 2014
IMPROVED ARTIFICIAL NEURAL NETWORK PERFORMANCE ON SURFACE OZONE PREDICTION USING PRINCIPAL COMPONENT ANALYSIS
Author: K. Padma, R. Samuel Selvaraj, S. Arputharaj, B. Milton Boaz
Correlated sample data normally creates confusion over ANN (Artificial Neural network) during the learning process. In this work the Principal Component Analysis (PCA) method is used for elimination of correlated terms in data. After application of PCA, the uncorrelated input data were used to train a Multi-Layer Perceptron (MLP) ANN system. The results revealed that the elimination of correlated information by using the PCA method improved the ANN estimation performance. we measured the surface ozone and its influencing factors during the period June 2011- September 2012 at Chennai, a tropical site on the Southeast coast of India situated at 13 04’N 80 17’E. The input data that was used for building the network are the wind speed, temperature, relative humidity, UV radiation had been used in neural networks for the prediction of daily surface Ozone 24 hours in advance.
Keywords: Multi-layer perceptron, Artificial neural network, Principal component analysis, activation function, hidden layers, ‘PC variance’
K. Padma, R. Samuel Selvaraj, S. Arputharaj, B. Milton Boaz. IMPROVED ARTIFICIAL NEURAL NETWORK PERFORMANCE ON SURFACE OZONE PREDICTION USING PRINCIPAL COMPONENT ANALYSIS International Journal of Current Research and Review. 6(16), August, 01-06
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