Journal of Global Optimization
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Information Sciences: an International Journal
Linearity testing for fuzzy rule-based models
Fuzzy Sets and Systems
Equivalences between neural-autoregressive time series models and fuzzy systems
IEEE Transactions on Neural Networks
IPADE: iterative prototype adjustment for nearest neighbor classification
IEEE Transactions on Neural Networks
Modern Applied Statistics with S
Modern Applied Statistics with S
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
A flexible coefficient smooth transition time series model
IEEE Transactions on Neural Networks
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This paper presents a procedure for parameter estimation of the neuro-coefficient smooth transition autoregressive model, substituting the combination of grid search and local search of the original proposal of Medeiros and Veiga (2005, IEEE Trans. NN, 16(1):97-113) with a differential evolution scheme. The purpose of this novel fitting procedure is to obtain more accurate models under preservation of the most important model characteristics. These are, firstly, that the models are built using an iterative approach based on statistical tests, and therewith have a mathematically sound construction procedure. And secondly, that the models are interpretable in terms of fuzzy rules. The proposed procedure has been tested empirically by applying it to different real-world time series. The results indicate that, in terms of accuracy, significantly improved models can be achieved, so that accuracy of the resulting models is comparable to other standard time series forecasting methods.