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In classic phase space reconstruction, the time lag is identical. In our research, the different time lags are found more effectively for teletraffic forecasting. In this paper, a method to determine the different time lags in phase space reconstruction is proposed. Simulation results show that the prediction is more accurate by using the different time lags in reconstruction phase space.
Radial basis function neural networks have been successfully applied to time series prediction in literature. Frequently, methods to build and train these networks must be given the past periods or lags to be used in order to create patterns and forecast any time series. This paper introduces E-tsRBF, a meta-evolutionary algorithm that evolves both the neural networks and the set of lags needed to...
This Paper presents a method for predicting power system voltage harmonics using Time-Delay Neural Network (TDNN). The TDNN method is based on the back propagation learning technique for feedforward neural networks. This approach has certain advantages over other conventional schemes, including the potential to track the harmonics in the time-varying power system environment. In order to demonstrate...
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