Abstract
This paper disposes towards an idea to develop a new network model called a Jordan Pi Sigma Neural Network (JPSN) to overcome the drawbacks of ordinary Multilayer Perceptron (MLP) whilst taking the advantages of Pi-Sigma Neural Network (PSNN). JPSN, a network model with a single layer of tuneable weights with a recurrent term added in the network, is trained using the standard backpropagation algorithm. The network was used to learn a set of historical temperature data of Batu Pahat region for five years (2005-2009), obtained from Malaysian Meteorological Department (MMD). JPSN’s ability to predict the future trends of temperature was tested and compared to that of MLP and the standard PSNN. Simulation results proved that JPSN’s forecast comparatively superior to MLP and PSNN models, with the combination of learning rate 0.1, momentum 0.2 and network architecture 4-2-1 andlower prediction error. Thus, revealing a great potential for JPSN as an alternative mechanism to both PSNN and MLP in predicting the temperature measurement for one-step-ahead.
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Husaini, N.A., Ghazali, R., Ismail, L.H., Herawan, T. (2014). A Jordan Pi-Sigma Neural Network for Temperature Forecasting in Batu Pahat Region. In: Herawan, T., Ghazali, R., Deris, M. (eds) Recent Advances on Soft Computing and Data Mining. Advances in Intelligent Systems and Computing, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-319-07692-8_2
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DOI: https://doi.org/10.1007/978-3-319-07692-8_2
Publisher Name: Springer, Cham
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