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In recent years, various temperature forecasting models have been proposed, which broadly can be classified into physically-based approaches and statistically-based approaches. Hitherto, those approaches involve sophisticated mathematical models to justify the use of empirical rules which make them less desirable for some applications. Therefore, in this respect, Neural Networks (NN) have been successfully applied and with no doubt, they provide the ability and potentials to predict the temperature events. However, the ordinary NN adopts computationally intensive training algorithms and can easily get trapped into local minima.To overcome such drawbacks in ordinary NN, this research focuses on using a

The main purpose of this study is to employ Pi-Sigma Neural Network (PSNN) for one-stepahead temperature forecasting. In this paper, we evaluate the performances of PSNN by comparing the network model with widely used Multilayer Perceptron (MLP). PSNN which is a class of Higher Order Neural Networks (HONN), has a highly regular structure, needs much smaller number of weights and less training time. The PSNN is use to overcome the drawbacks of MLP, which can easily trapped into local minima and prone to over¯t. Both network models were trained with standard backpropagation algorithm. Through 1012 experiments, it has been demonstrated that the PSNN has a high practicability and better temperature forecasting for one-step-ahead using historical temperature data of Batu Pahat region.

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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.