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 (2011) Temperature forecasting with a dynamic higher-order neural network model. In: iiWAS '11: Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services, 5–7 December 2011, Ho Chi Minh City, Vietnam.

Full text not available from this repository.

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:Conference or Workshop Item (Paper)
Uncontrolled Keywords:temperature; higher order; recurrent; Jordan Pi-Sigma
Subjects:T Technology > TJ Mechanical engineering and machinery > TJ212-225 Control engineering systems. Automatic machinery (General)
Divisions:Faculty of Computer Science and Information Technology > Department of Software Engineering
ID Code:2981
Deposited By:Normajihan Abd. Rahman
Deposited On:22 Jan 2013 18:22
Last Modified:21 Jan 2015 15:13

Repository Staff Only: item control page