Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Ensembling neural networks: many could be better than all
Artificial Intelligence
Fuzzy modeling using generalized neural networks and Kalman filter algorithm
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
Expert Systems with Applications: An International Journal
Construction of a new BRB based model for time series forecasting
Applied Soft Computing
Generalized dynamical fuzzy model for identification and prediction
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Information Sciences: an International Journal
Hi-index | 12.05 |
This paper describes an architecture for ensembles of ANFIS (adaptive network based fuzzy inference system), with emphasis on its application to the prediction of chaotic time series, where the goal is to minimize the prediction error. The time series that we are considered are: the Mackey-Glass, Dow Jones and Mexican stock exchange. The methods used for the integration of the ensembles of ANFIS are: integrator by average and the integrator by weighted average. The performance obtained with this architecture overcomes several standard statistical approaches and neural network models reported in the literature by various researchers. In the experiments we changed the type of membership functions and the desired goal error, thereby increasing the complexity of the training.