A new fast forecasting technique using high speed neural networks

Forecasting is an important issue for many different applications. In this paper, a new efficient forecasting technique is presented. Such technique is designed by using fast neural networks (FNNs). The new idea relies on performing cross correlation in the frequency domain between the input data and the input weights of neural networks. It is proved mathematically and practically that the number of computation steps required for the proposed fast forecasting technique is less than that needed by conventional neural-based forecasting. Simulation results using MATLAB confirm the theoretical computations. The proposed fast forecasting technique increases the prediction speed and at the same time does not affect the predication accuracy. It is applied professionally for erythemal ultraviolet irradiance prediction.

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