Prediction of chaotic time series using computational intelligence

Authors:
B. Samanta
Affiliations:
Department of Mechanical Engineering, Villanova University, Villanova, PA 19085, USA
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 6
Cited 3

Time Series Analysis: Forecasting and Control

Time Series Analysis: Forecasting and Control
On the complexity of computing and learning with multiplicative neural networks

Neural Computation
Time series prediction with single multiplicative neuron model

Applied Soft Computing
Prognostics of machine condition using soft computing

Robotics and Computer-Integrated Manufacturing
PSO-based single multiplicative neuron model for time series prediction

Expert Systems with Applications: An International Journal
Use of particle swarm optimization for machinery fault detection

Engineering Applications of Artificial Intelligence

A New Multiplicative Seasonal Neural Network Model Based on Particle Swarm Optimization

Neural Processing Letters
Short, medium and long term forecasting of time series using the L-Co-R algorithm

Neurocomputing
Generalized dynamical fuzzy model for identification and prediction

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

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Abstract

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.