A Hybrid Model Based on Ensemble Empirical Mode Decomposition and Fruit Fly Optimization Algorithm for Wind Speed Forecasting

Figure 1

A structure schematic chart of GRNN (where , is the input variable of the network, is a training vector of the th neuron in the pattern layer, denotes the smoothing parameter (also called spread parameter), is the measured value of the output variable, is the pattern Gaussian function, and are the network weights, and are the signals from summation neurons, and is the network output).