Structure identification and parameter optimization for non-linear fuzzy modeling
Fuzzy Sets and Systems - Fuzzy systems
Interpretability constraints for fuzzy information granulation
Information Sciences: an International Journal
International Journal of Approximate Reasoning
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Cascaded centralized TSK fuzzy system: universal approximator and high interpretation
Applied Soft Computing
Knowledge-based parameter identification of TSK fuzzy models
Applied Soft Computing
TaSe, a Taylor series-based fuzzy system model that combines interpretability and accuracy
Fuzzy Sets and Systems
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
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - 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
New Online Self-Evolving Neuro Fuzzy controller based on the TaSe-NF model
Information Sciences: an International Journal
Navigating interpretability issues in evolving fuzzy systems
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
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We present a form of fuzzy inference systems (FISs) that is highly interpretable and easy to manipulate. The form is based on a judicious choice of membership functions that have strong locality and differentiability properties and on a modification of the Sugeno and generalized Sugeno forms of the consequent polynomials so as to make them rule centered. Under these conditions, the coefficients in the consequent polynomials can be exactly interpreted as Taylor series coefficients. Besides the intuitive interpretation thus bestowed on the coefficients, we show that the new form allows easy design, manipulation, testing, training, and combination of the resulting fuzzy inference systems. The rudiments of a calculus of fuzzy inference systems are then introduced