Structure identification of fuzzy model
Fuzzy Sets and Systems
Sugeno type controllers are universal controllers
Fuzzy Sets and Systems
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
An introduction to fuzzy control (2nd ed.)
An introduction to fuzzy control (2nd ed.)
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Fuzzy modelling and identification with genetic algorithm based learning
Fuzzy Sets and Systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Soft Computing and Fuzzy Logic
IEEE Software
Hierarchical neuro-fuzzy quadtree models
Fuzzy Sets and Systems - Fuzzy models
Structure and parameter learning of neuro-fuzzy systems: A methodology and a comparative study
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Similarity measures in fuzzy rule base simplification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A systematic approach to a self-generating fuzzy rule-table forfunction approximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An approach to online identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Rule-based modeling: fast construction and optimal manipulation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Forecasting time series with genetic fuzzy predictor ensemble
IEEE Transactions on Fuzzy Systems
Improving the interpretability of TSK fuzzy models by combining global learning and local learning
IEEE Transactions on Fuzzy Systems
A highly interpretable form of Sugeno inference systems
IEEE Transactions on Fuzzy Systems
Self-organized fuzzy system generation from training examples
IEEE Transactions on Fuzzy Systems
On multistage fuzzy neural network modeling
IEEE Transactions on Fuzzy Systems
Designing fuzzy inference systems from data: An interpretability-oriented review
IEEE Transactions on Fuzzy Systems
Structure identification in complete rule-based fuzzy systems
IEEE Transactions on Fuzzy Systems
Multiobjective identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Fuzzy Systems
Comparison of adaptive methods for function estimation from samples
IEEE Transactions on Neural Networks
A new clustering technique for function approximation
IEEE Transactions on Neural Networks
International Journal of Approximate Reasoning
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
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Logic-based fuzzy networks: A study in system modeling with triangular norms and uninorms
Fuzzy Sets and Systems
Knowledge-based parameter identification of TSK fuzzy models
Applied Soft Computing
Efficient Optimization of the Parameters of LS-SVM for Regression versus Cross-Validation Error
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Local-global neuro-fuzzy system for color change modelling
International Journal of Hybrid Intelligent Systems - Advances in Intelligent Agent Systems
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Effective input variable selection for function approximation
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Analysis of the TaSe-II TSK-Type fuzzy system for function approximation
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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
New Online Self-Evolving Neuro Fuzzy controller based on the TaSe-NF model
Information Sciences: an International Journal
A 2uFunction representation for non-uniform type-2 fuzzy sets: Theory and design
International Journal of Approximate Reasoning
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Typically, Takagi-Sugeno-Kang (TSK) fuzzy rules have been used as a powerful tool for function approximation problems, since they have the capability of explaining complex relations among variables using rule consequents that are functions of the input variables. But they present the great drawback of the lack of interpretability, which makes them not to be so suitable for a wide range of problems where interpretability of the obtained model is a fundamental key. In this paper, we present a novel approach that extends the work by Bikdash (IEEE Trans. Fuzzy Systems 7 (6) (1999) 686-696), in order to obtain an interpretable and accurate model for function approximation from a set of I/O data samples, which make use of the Taylor Series Expansion of a function around a point to approximate the function using a low number of rules. Our approach also provides an automatic methodology for obtaining the optimum structure of our Taylor series-based (TaSe) fuzzy system as well as its pseudo-optimal rule-parameters (both antecedents and consequents).