International Journal of Knowledge-based and Intelligent Engineering Systems
Global and Local Modelling in Radial Basis Functions Networks
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Rapid load following of an SOFC power system via stable fuzzy predictive tracking controller
IEEE Transactions on Fuzzy Systems
TaSe, a Taylor series-based fuzzy system model that combines interpretability and accuracy
Fuzzy Sets and Systems
LMI based design of constrained fuzzy predictive control
Fuzzy Sets and Systems
On reducing computational overhead in multi-objective genetic Takagi-Sugeno fuzzy systems
Applied Soft Computing
Observer design for nonlinear systems represented by Takagi-Sugeno models
WSEAS TRANSACTIONS on SYSTEMS
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Local-global neuro-fuzzy system for color change modelling
International Journal of Hybrid Intelligent Systems - Advances in Intelligent Agent Systems
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Clustering-Based TSK neuro-fuzzy model for function approximation with interpretable sub-models
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
TaSe model for long term time series forecasting
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
A Neuro-Fuzzy Identification of ECG Beats
Journal of Medical Systems
New Online Self-Evolving Neuro Fuzzy controller based on the TaSe-NF model
Information Sciences: an International Journal
Nonlinear system identification of large-scale smart pavement systems
Expert Systems with Applications: An International Journal
Hybrid-fuzzy modeling and identification
Applied Soft Computing
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The problem of identifying the parameters of the constituent local linear models of Takagi-Sugeno fuzzy models is considered. In order to address the tradeoff between global model accuracy and interpretability of the local models as linearizations of a nonlinear system, two multiobjective identification algorithms are studied. Particular attention is paid to the analysis of conflicts between objectives, and we show that such information can be easily computed from the solution of the multiobjective optimization. This information is useful to diagnose the model and tune the weighting/priorities of the multiobjective optimization. Moreover, the result of the conflict analysis can be used as a constructive tool to modify the fuzzy model structure (including membership functions) in order to meet the multiple objectives. Simple illustrative examples as well as experimental results show the usefulness of the method.