**6. References**

Al-Badi, A., Ellithy, K. & Al-Alawi, S., (2005), "An Artificial Neural Network model for Predicting gas pipeline Induced Voltage caused by Power Lines under Fault

**13** 

*Malaysia* 

**Series Signal** 

Rozaida Ghazali, Noor Aida Husaini,

*Universiti Tun Hussein Onn Malaysia* 

Lokman Hakim Ismail and Noor Azah Samsuddin

**An Application of Jordan Pi-Sigma Neural** 

**Network for the Prediction of Temperature Time** 

Temperature forecasting is mainly issued in qualitative terms with the use of conventional methods, assisted by the data projected images taken by meteorological satellites to assess future trends (Paras *et al.*, 2007). Several criteria that need to be considered when choosing a forecasting method include the accuracy, the cost and the properties of the series being forecast. Considering those criteria, it is noted that such empirical approaches that has been conducted for temperature forecasting is intrinsically costlier and only proficient of providing certain information, which is usually generalized over a larger geographical area (Paras *et al.*, 2007). Despite of involving sophisticated mathematical models to justify the use of empirical rules, it also requires a prior knowledge of the characteristics of the input time-series to predict future events. Not only that, most temperature forecasts today have limited information about uncertainty. Yet, meteorologists often find it challenging to communicate uncertainty effectively. Regardless of the extensive use of the numerical weather method, they are still restricted by the availability of numerical weather prediction products, leading to various studies being conducted for temperature forecasting (Barry &

Due to that inadequacy, Neural Network (NN) has been applied in such temperature forecasting. NN mimic human intelligence in learning from complicated or imprecise data and can be used to extract patterns and detect trends that are too complex to be perceived by humans and other computer techniques (Mielke, 2008). NN which can be described as an adaptive machine that has a natural tendency for storing experiential knowledge, are able to discover complex nonlinear relationships in the meteorological processes by communicating forecast uncertainty that relates the forecast data to the actual weather (Chang *et al.*, 2010). However, when the number of inputs to the model and the number of training examples becomes extremely large, the training procedure for ordinary neural network, especially the Multilayer Perceptron (MLP) becomes tremendously slow and unduly tedious. Indeed, MLP are prone to overfit the data (Radhika & Shashi, 2009) and adopts computationally intensive training algorithms. On the other hand, MLP also suffer long training times and often reach

**1. Introduction** 

Chorley, 1982; Paras *et al.*, 2007)

local minima (Ghazali & al-Jumeily, 2009).

Conditions", *COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering*, Vol. 24, No. 1, (2005), pp. 69-80,ISSN: 0332-1649.

