A fuzzy constraint-based agent negotiation with opponent learning
ACOS'07 Proceedings of the 6th Conference on WSEAS International Conference on Applied Computer Science - Volume 6
Operation properties and δ-equalities of complex fuzzy sets
International Journal of Approximate Reasoning
Propagation of Random Perturbations under Fuzzy Algebraic Operators
KSEM '09 Proceedings of the 3rd International Conference on Knowledge Science, Engineering and Management
Optimal fuzzy reasoning methods based on robust goals/constraints
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Modeling opponent's beliefs via fuzzy constraint-directed approach in agent negotiation
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
A feedback based CRI approach to fuzzy reasoning
Applied Soft Computing
On the robustness of Type-1 and Interval Type-2 fuzzy logic systems in modeling
Information Sciences: an International Journal
Robustness of interval-valued fuzzy inference
Information Sciences: an International Journal
Perturbation of fuzzy sets and fuzzy reasoning based on normalized Minkowski distances
Fuzzy Sets and Systems
Impacts of perturbations of training patterns on two fuzzy associative memories based on t-norms
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Robustness analysis of full implication inference method
International Journal of Approximate Reasoning
On robustness of the full implication triple I inference method with respect to finer measurements
International Journal of Approximate Reasoning
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Fuzzy reasoning methods (or approximate reasoning methods) are extensively used in intelligent systems and fuzzy control. In this paper the author discusses how errors in premises affect conclusions in fuzzy reasoning, that is, he discusses the robustness of fuzzy reasoning. After reviewing his previous work (1996), he presents robustness results for various implication operators and inference rules. All the robustness results are formulated in terms of δ-equalities of fuzzy sets. Two fuzzy sets are said to be δ-equal if they are equal to an extent of δ