Abstract
This article is addressed to knowledge modelling and formalization using a fuzzified knowledge unit. The work is based on the system approach to the definition of knowledge units, on the procedural form of knowledge. Fuzzification of knowledge units draws innovation potential from knowledge units with fuzzy linguistic variables and Mamdani fuzzy inference system. Fuzzy knowledge units arise as a join the best properties of the given approaches. The core is the knowledge unit itself comprising the description of a problem and its solution. The typical knowledge unit consists of four elements – X as problem situation, Y as elementary problem, Z as goal of elementary problem solving and Q as solution of elementary problem. A last element of a knowledge unit Q is fuzzified by fuzzy linguistic variable. Steps of fuzzification process are described in the case study “Process customization.” The discussion unifies the findings from the chapters with results of the case study.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
