Predicting Natural and Chaotic Time Series with a Swarm-Optimized Neural Network
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摘要:
Natural and chaotic time series are predicted using an artificial neural network (ANN) based on particle swarm optimization (PSO).Firstly,the hybrid ANN+PSO algorithm is applied on Mackey-Glass series in the short-term prediction x(t + 6),using the current value x(t) and the past values:x(t - 6),x(t - 12),x(t - 18).Then,this method is applied on solar radiation data using the values of the past years:x(t - 1),...,x(t - 4).The results show that the ANN+PSO method is a very powerful tool for making predictions of natural and chaotic time series.Chaotic time series is an important research and application area.Several models for time series data can have many forms and represent different stochastic processes.Time series contain much information about dynamic systems.[1] These systems are usually modeled by delay-differential equations.[2]