Temperature forecasting with a dynamic higher-order neural network model

Husaini, Noor Aida and Ghazali, Rozaida and Ismail, Lokman Hakim and Abdul Hamid, Norhamreeza and Mat Deris, Mustafa and Mohd Nawi, Nazri Temperature forecasting with a dynamic higher-order neural network model. iiWAS '11 Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services . p. 2011.

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Official URL: http://dl.acm.org/citation.cfm?doid=2095536.209565...

Abstract

This paper presents the application of a combined approach of Higher Order Neural Networks and Recurrent Neural Networks, so called Jordan Pi-Sigma Neural Network (JPSN) for comprehensive temperature forecasting. In the present study, one-step-ahead forecasts are made for daily temperature measurement, by using a 5-year historical temperature measurement data. We also examine the effects of network parameters viz the learning factors, the higher order terms and the number of neurons in the input layer for selecting the best network architecture, using several performance measures. The comparison results show that the JPSN model can provide excellent fit and forecasts with reasonable results, therefore can be used as temperature forecasting tool.

Item Type:Article
Uncontrolled Keywords:Jordan Pi-Sigma; higher order; recurrent; temperature
Subjects:T Technology > T Technology (General)
ID Code:3129
Deposited By:Muhamad Azrul Hisam Salleh
Deposited On:07 Nov 2012 12:12
Last Modified:07 Nov 2012 12:12

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