Title :
Genetic optimization of ensemble neural networks for complex time series prediction
Author :
Pulido, M. ; Melin, P. ; Castillo, O.
Author_Institution :
Comput. Sci., Tijuana Inst. of Technol., Tijuana, Mexico
fDate :
July 31 2011-Aug. 5 2011
Abstract :
This paper describes an optimization method for ensemble neural network models with fuzzy aggregation of responses for forecasting complex time series using genetic algorithms. The time series under consideration for testing the hybrid approach is the Mackey-Glass data, and results for the optimization of type-1 fuzzy response aggregation in the ensemble neural network are presented. Simulation results show the effectiveness of the proposed approach.
Keywords :
complex networks; forecasting theory; fuzzy set theory; genetic algorithms; neural nets; prediction theory; time series; Mackey-Glass data; complex time series prediction; ensemble neural network models; genetic algorithms; optimization method; type-1 fuzzy response aggregation; Fuzzy logic; Fuzzy systems; Genetic algorithms; Neural networks; Neurons; Time series analysis; Training;
Conference_Titel :
Neural Networks (IJCNN), The 2011 International Joint Conference on
Conference_Location :
San Jose, CA
Print_ISBN :
978-1-4244-9635-8
DOI :
10.1109/IJCNN.2011.6033222
