کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
385586 | 660868 | 2011 | 6 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Prediction of chaotic time series using computational intelligence Prediction of chaotic time series using computational intelligence](/preview/png/385586.png)
In this paper, two CI techniques, namely, single multiplicative neuron (SMN) model and adaptive neuro-fuzzy inference system (ANFIS), have been proposed for time series prediction. A variation of particle swarm optimization (PSO) with co-operative sub-swarms, called COPSO, has been used for estimation of SMN model parameters leading to COPSO-SMN. The prediction effectiveness of COPSO-SMN and ANFIS has been illustrated using commonly used nonlinear, non-stationary and chaotic benchmark datasets of Mackey–Glass, Box–Jenkins and biomedical signals of electroencephalogram (EEG). The training and test performances of both hybrid CI techniques have been compared for these datasets.
► Prediction of chaotic time series using two hybridCI techniques is considered.
► Single multiplicative neuron (SMN) model is used in place of conventional ANN.
► SMN model parameters are estimated using cooperative particle swam optimization.
► Results are compared with adaptive neuro-fuzzy inference system (ANFIS).
► Both show good results but ANFIS performs better for all three benchmark datasets.
Journal: Expert Systems with Applications - Volume 38, Issue 9, September 2011, Pages 11406–11411