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Doctoral Thesis / Dissertation from the year 2010 in the subject Engineering - Artificial Intelligence, grade: PhD, Korea University, Seoul (College of Engineering - Dept of Industrial Systems and Information Engineering), course: Intelligence Control and Artificial Intelligence, language: English, abstract: Fuzzy Logic (FL) is a particular area of interest in the study of Artificial intelligence
(AI) based on the idea that in fuzzy sets each element in the set can assume a value from
0 to 1, not just 0 or 1, as in classic or crisp set theory. The gradation in the extent to
which an element is belonging to the relevant sets is called the degree of membership.
This degree of membership is a measure of the element's belonging to the set, and thus of
the precision with which it explains the phenomenon being evaluated. A linguistic
expression is given to each fuzzy set. The information contents of the fuzzy rules are then
used to infer the output using a suitable inference engine. The key contribution of fuzzy
logic in computation of information described in natural language made it applicable to a
variety of applications and problem domains; from simple control systems to human
decision support systems. Yet, despite its long-standing origins, it is a relatively new field,
and as such leaves much room for development.
The thesis presents two novel applications of fuzzy systems; a human decision
support system to help teachers to fairly evaluate students and two hybrid intelligent
fuzzy systems; a type-2 fuzzy logic system and a combined type-1 fuzzy logic system and
extended Kalamn filter for controlling systems operating under high levels of
uncertainties due to various sources of measurement and modeling errors.
The combination of fuzzy logic and the classical student evaluation approach
produces easy to understand transparent decision model that can be easily understood by
students and teachers alike. The developed architecture overcomes the problem of
ranking studen
(AI) based on the idea that in fuzzy sets each element in the set can assume a value from
0 to 1, not just 0 or 1, as in classic or crisp set theory. The gradation in the extent to
which an element is belonging to the relevant sets is called the degree of membership.
This degree of membership is a measure of the element's belonging to the set, and thus of
the precision with which it explains the phenomenon being evaluated. A linguistic
expression is given to each fuzzy set. The information contents of the fuzzy rules are then
used to infer the output using a suitable inference engine. The key contribution of fuzzy
logic in computation of information described in natural language made it applicable to a
variety of applications and problem domains; from simple control systems to human
decision support systems. Yet, despite its long-standing origins, it is a relatively new field,
and as such leaves much room for development.
The thesis presents two novel applications of fuzzy systems; a human decision
support system to help teachers to fairly evaluate students and two hybrid intelligent
fuzzy systems; a type-2 fuzzy logic system and a combined type-1 fuzzy logic system and
extended Kalamn filter for controlling systems operating under high levels of
uncertainties due to various sources of measurement and modeling errors.
The combination of fuzzy logic and the classical student evaluation approach
produces easy to understand transparent decision model that can be easily understood by
students and teachers alike. The developed architecture overcomes the problem of
ranking studen
- Format: Pocket/Paperback
- ISBN: 9783656152934
- Språk: Engelska
- Antal sidor: 100
- Utgivningsdatum: 2012-03-19
- Förlag: Grin Verlag