Learning from hints in neural networks
Journal of Complexity
Symbolic-neural systems and the use of hints for developing complex systems
International Journal of Man-Machine Studies
Training second-order recurrent neural networks using hints
ML92 Proceedings of the ninth international workshop on Machine learning
Construction of fuzzy classification systems with rectangular fuzzy rules using genetic algorithms
Fuzzy Sets and Systems - Special issue on fuzzy methods for computer vision and pattern recognition
Neural Computation
Fuzzy model identification: selected approaches
Fuzzy model identification: selected approaches
Fuzzy systems modeling in practice
Fuzzy Sets and Systems - Special issue on formal methods for fuzzy modeling and control
Fuzzy Modeling for Control
Functional Networks with Applications: A Neural-Based Paradigm
Functional Networks with Applications: A Neural-Based Paradigm
Commutativity as prior knowledge in fuzzy modeling
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
Back-driving a truck with suboptimal distance trajectories: a fuzzy logic control approach
IEEE Transactions on Fuzzy Systems
Strategies to identify fuzzy rules directly from certainty degrees: a comparison and a proposal
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
Monotone Mamdani--Assilian models under mean of maxima defuzzification
Fuzzy Sets and Systems
IEEE Transactions on Fuzzy Systems
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In fuzzy modeling (FM), the quantity and quality of the training set is crucial to properly grasp the behavior of the system being modeled. However, the available data are often not large enough or are deficiently distributed along the input space, not revealing the system behavior completely. In such cases, the consideration of any prior knowledge about the system can be decisive for the accuracy achieved by the fuzzy modeling. This paper faces with the integration of mathematical properties satisfied by a system as prior knowledge in FM, focusing on the commutativity property as a starting point. With this aim, several measures are developed to evaluate the commutativity in a fuzzy environment dealing with different elements involved in FM. Then, several approaches are proposed to measure the commutativity degrees of a fuzzy rule with respect to the training set and a simple method is presented to integrate these degrees into the FM task. The experimental results show the accuracy improvement gained by the proposed method.