Performance Comparison of Different Multilayer Perceptron Network Activation Functions in Automated Weather Classification

Multilayer perceptron network (MLP) has been recognized as a powerful tool for many applications including classification. Selection of the activation functions in the multilayer perceptron (MLP) network plays an essential role on the network performance. This paper presents a comparison study of two commonly used MLP activation function, sigmoid and hyperbolic tangent for weather classification. Meteorological data such as solar radiation, ambient temperature, current, surface temperature, voltage, wind direction and wind speed data are acquired from a photovoltaic (PV) system. Then, the meteorological data are input to the MLP network to classify the weather condition. In this study, weather conditions are classified into four types, rain, cloudy, dry day and storm. Levenberg-Marquardt algorithm is used to train the MLP network since it is the fastest training and ensure the best converges towards a minimum error. Experimental results show that hyperbolic tangent activation function is more efficient compared to sigmoid activation function. The MLP network using hyperbolic tangent function has achieved higher classification accuracy with less number of hidden nodes compared to sigmoid activation function.