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Echo state network (ESN), a novel recurrent neural network, has a randomly and sparsely connected reservoir. Since the reservoir is very large, the collinearity problem may exist in ESN. To overcome this problem and get a sparse architecture, an adaptive lasso echo state network (ALESN) is proposed, in which the adaptive lasso algorithm is used to calculate the output weights. The proposed ALESN can...
The leaky integrator echo state network (Leaky-ESN), an improved echo state network (ESN), has been proved to be useful for learning very low dynamic systems. However, the Leaky-ESN exists a complex nonlinear relationship between parameters, and then setting parameter values is very difficult because of lacking effective guiding principles. It is difficult for beginners to give suitable values of...
Limited historical data and large fluctuations are two important issues for forecasting time series. In this paper, a hybrid forecasting model based on adaptive fuzzy time series and particle swarm optimization is proposed to address these issues. In the training phase, the heuristic rules automatically adapt the forecasted values based on trend values and the particle swarm optimization is applied...
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