Structure identification of fuzzy model
Fuzzy Sets and Systems
An introduction to fuzzy control (2nd ed.)
An introduction to fuzzy control (2nd ed.)
Predicting a chaotic time series using a fuzzy neural network
Information Sciences: an International Journal
Neuro-fuzzy systems for function approximation
Fuzzy Sets and Systems - Special issue on analytical and structural considerations in fuzzy modeling
Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological, and Engineering Systems
Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological, and Engineering Systems
Introductory Econometrics: Using Monte Carlo Simulation with Microsoft Excel
Introductory Econometrics: Using Monte Carlo Simulation with Microsoft Excel
Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications (Advances in Industrial Control)
Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models
Expert Systems with Applications: An International Journal
Choice of conjunctive operator of TSK fuzzy systems and stability domain study
Mathematics and Computers in Simulation
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
Modeling uncertainty in clinical diagnosis using fuzzy logic
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Modeling gunshot bruises in soft body armor with an adaptive fuzzy system
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A flexible coefficient smooth transition time series model
IEEE Transactions on Neural Networks
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Heteroscedasticy is the property of having a changing variance throughout the time. Homoscedasticity is the converse, that is, having a constant variance. This is a key property for time series models which may have serious consequences when making inferences out of the errors of a given forecaster. Thus it has to be conveniently assessed in order to establish the quality of the model and its forecasts. This is important for every model including fuzzy rule-based systems, which have been applied to time series analysis for many years. Lagrange multiplier testing framework is used to evaluate wether the residuals of an FRBS are homoscedastic. The test robustness is thoroughly evaluated through an extensive experimentation. This is another important step towards a statistically sound modeling strategy for fuzzy rule-based systems.