**6. Conclusion**

There are many applications and techniques on temperature that was developed in the past. However, limitations such as the accuracy and complexity of the models make the existing system less enviable for some applications. Therefore, improvement on temperature forecasting requires continuous efforts in many fields, including NN. Several methods related to NN, particularly have been investigated and carried out. However, the ordinary feedforward NN, the MLP, is prone to overfitting and easily get stuck into local minima. Thus, to overcome the drawbacks, a new model, called JPSN is proposed as an alternative mechanism to predict the temperature event. The JPSN which combines the properties of PSNN and RNN can benefits the temperature prediction event, which may overcome such drawbacks in MLP. In this chapter, JPSN is used to learn the historical temperature data of Batu Pahat, and to predict the temperature measurements for the next-day ahead. Simulations for the comprehensive evaluation of the JPSN were presented, and the evaluation covering several performance criteria: the NMSE, MSE, SNR, and number of epoch were discussed. Experimental results of JPSN were compared with the ordinary PSNN and the MLP. Results obtained from each model were presented, and on the whole, the proposed JPSN has shown to outperform the ordinary PSNN and MLP on the prediction errors and convergence time.
