Current Proceedings on Technology

Yazarlar: Noor Aida Husaini, Rozaida Ghazali, Nazri Mohd Nawi, Lokman Hakim Ismail

Konular:-

Anahtar Kelimeler:Temperature Forecasting,Prediction,Jordan Pi-Sigma,Multilayer Perceptron,Neural Network  

Özet:   This paper presents an optimal higher order to forecast temperature event in Batu Pahat, Malaysia by using a Jordan Pi-Sigma Neural Network (JPSN). There are many conventional techniques in dealing with forecasting meteorological issue; however, there are some shortcoming noticed in terms of accuracy and tractability. To solve these problems, we consider evaluating the effects of higher order terms in JPSN for temperature forecasting. The data of temperature measurement in Batu Pahat has been used in order to validate the network model by utilising the backpropagation training algorithm. The results of the prediction made by JPSN were compared with the widely known Multilayer Perceptron. Towards the end, we found that the JPSN of Order 2 gives the best results in predicting the next-day ahead prediction, thus can be used for temperature forecasting with acceptable lower prediction error.

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