Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
A Parametric Approach to Deductive Databases with Uncertainty
IEEE Transactions on Knowledge and Data Engineering
Nonsmooth training of fuzzy neural networks
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Adaptation of weighted fuzzy programs
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Scalable querying services over fuzzy ontologies
Proceedings of the 17th international conference on World Wide Web
Connectionist weighted fuzzy logic programs
Neurocomputing
Adaptation of Connectionist Weighted Fuzzy Logic Programs with Kripke-Kleene Semantics
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Integrated Query Answering with Weighted Fuzzy Rules
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Top-down computation of the semantics of weighted fuzzy logic programs
RR'07 Proceedings of the 1st international conference on Web reasoning and rule systems
Adaptation of weighted fuzzy programs
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
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Fuzzy logic programs are a useful framework for handling uncertainty in logic programming; nevertheless, there is the need for modelling adaptation of fuzzy logic programs. In this paper, we first overview weighted fuzzy programs, which bring fuzzy logic programs and connectionist models closer together by associating significance weights with the atoms of a logic rule: by exploiting the existence of weights, it is possible to construct a neural network model that reflects the structure of a weighted fuzzy program. Based on this model, we then introduce the weighted fuzzy program adaptation problem and propose an algorithm for adapting the weights of the rules of the program to fit a given dataset.