Generators of Fuzzy Operations for Hardware Implementation of Fuzzy Systems
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Parametric Operations for Digital Hardware Implementation of Fuzzy Systems
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
VLSI implementation of a module for realization of basic t-norms on fuzzy hardware
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Generalized fuzzy operations for digital hardware implementation
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Modular neuro-fuzzy systems based on generalized parametric triangular norms
PPAM'09 Proceedings of the 8th international conference on Parallel processing and applied mathematics: Part I
FPGA implementation of fuzzy system with parametric membership functions and parametric conjunctions
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
Aggregation procedures in intelligent systems
MACMESE'07 Proceedings of the 9th WSEAS international conference on Mathematical and computational methods in science and engineering
The theory of pseudo-linear operators
Knowledge-Based Systems
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An approach to fuzzy modeling based on the tuning of parametric conjunction operations is proposed. First, some methods for the construction of parametric generalized conjunction operations simpler than the known parametric classes of conjunctions are considered and discussed. Second, several examples of function approximation by fuzzy models, based on the tuning of the parameters of the new conjunction operations, are given and their approximation performances are compared with the approaches based on a tuning of membership functions and other approaches proposed in the literature. It is seen that the tuning of the conjunction operations can be used for obtaining fuzzy models with a sufficiently good performance when the tuning of membership functions is not possible or not desirable.