Fast learning in networks of locally-tuned processing units
Neural Computation
TaSe, a Taylor series-based fuzzy system model that combines interpretability and accuracy
Fuzzy Sets and Systems
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
A systematic approach to a self-generating fuzzy rule-table forfunction approximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy sets of rules for system identification
IEEE Transactions on Fuzzy Systems
A highly interpretable form of Sugeno inference systems
IEEE Transactions on Fuzzy Systems
Designing fuzzy inference systems from data: An interpretability-oriented review
IEEE Transactions on Fuzzy Systems
Multiobjective identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Fuzzy Systems
A new clustering technique for function approximation
IEEE Transactions on Neural Networks
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
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The fuzzy Takagi-Sugeno-Kang model and the inference system proposed by these authors is a very powerful tool for function approximation problems due to its capability of expressing a complex nonlinear system using a set of simple linear rules. Nevertheless, during the learning and optimization process, usually a trade-off has to be carried out among global system accuracy and sub-models (rules) interpretability. In this paper we review the TaSe model [8] for function approximation (for Grid-Based Fuzzy Systems and extend it to consider Clustering-Based Fuzzy Systems) that is learned from an I/O numerical data set and that will allow us to extract strong interpretable rules, whose consequents are the Taylor Series Expansion of the model output around the rule centres. This TaSe model provides full interpretability to the local models with high accuracy in the global approximation. The rule extraction process using the TaSe model and its properties will be reviewed using a significant example.