Prediction of chaotic time series using computational intelligence

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.