Paper Info
Title
Optimization of the type-1 and interval type-2 fuzzy integrators in Ensembles of ANFIS models for prediction of the Dow Jones time series
Abstract
This paper describes the optimization of interval type-2 fuzzy integrators in Ensembles of ANFIS (adaptive neuro-fuzzy inferences systems) models for the prediction of the Dow Jones time series. The Dow Jones time series is used to the test of performance of the proposed ensemble architecture. We used the interval type-2 and type-1 fuzzy systems to integrate the output (forecast) of each Ensemble of ANFIS models. Genetic Algorithms (GAs) were used for the optimization of membership function parameters of each interval type-2 fuzzy integrator. In the experiments we optimized Gaussian, Generalized Bell and Triangular membership functions parameter for each of the fuzzy integrators, thereby increasing the complexity of the training. Simulation results show the effectiveness of the proposed approach.
Year
DOI
Venue
2014
10.1109/CIDM.2014.7008666
Computational Intelligence and Data Mining
Keywords
Field
DocType
fuzzy reasoning,fuzzy set theory,genetic algorithms,mathematics computing,neural nets,time series,ANFIS models,Dow Jones time series prediction,GA,adaptive neurofuzzy inferences systems,ensemble architecture,fuzzy integrator optimization,genetic algorithm,membership function parameters,ANFIS Models,Dow Jones Time Series,Genetic Algorithms,interval type-2 and type-1 Fuzzy system
Neuro-fuzzy,Defuzzification,Fuzzy classification,Computer science,Fuzzy set operations,Fuzzy logic,Artificial intelligence,Adaptive neuro fuzzy inference system,Fuzzy control system,Membership function,Machine learning
Conference
Citations 
PageRank 
References 
2
0.37
16
Authors
3
Name
Order
Citations
PageRank
Jesus Soto11176.10
Patricia Melin24009259.43
Oscar Castillo35289452.83