Titlebook: Advances in Fuzzy Logic, Neural Networks and Genetic Algorithms; IEEE/Nagoya-Universi Takeshi Furuhashi Conference proceedings 1995 Springe

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https://doi.org/10.1007/978-1-0716-2835-5 of the fuzzy controller. In this construction, each membership function consists of a gaussian potential function (GPF) which causes the utilization of a reduced number of labels, and eventually the complexity of structural design becomes simple, specially for large scale inputs. This in turn reduc

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DAMP

https://doi.org/10.1007/978-3-319-14060-5s paper presents the issues related to Sugeno‘s fuzzy integral for information aggregation. For the identification of importance degrees of evaluation items with the properties of fuzzy measures, we suggest to use a genetic algorithm based method. To improve the behavior of the fuzzy integral by avo

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https://doi.org/10.1007/978-3-319-14060-5systems in the area of pattern classification. In this paper we present an efficient method for combining the modular networks based on fuzzy logic, especially the fuzzy integral. This method nonlinearly combines objective evidences, in the form of network outputs, with subjective evaluation of the

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Astrid E. Cardona,Eroboghene E. Ubogus operators (.), 4 knowledge based rules were listed up. Especially, it was found that Alcohol-Baumé plot and BMD curve were important as a reference for making . with good quality. Fermentation period was separated into 4 control regions. Fuzzy production rules and membership functions for temperat