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Course title is Embedded Intelligent Robotics. This course is for Electrical engineering students. Though good thing is everyone can learn about robotics in this course. This lecture includes: Fuzzy and Decomposition, Minimization of Fuzzy Functions, Fuzzy Decision Diagrams, Functional Decomposition, Identities, Transformations, Fuzzy Intersection, Decomposition Model, Ashenhurst Functional Decomposition, Column Multiplicity, Lattice of Two Variables, Graphical Representations
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Graphical Representations
Identities
Differences Between Boolean Logic and Fuzzy Logic Boolean logic the value of a variable and its inverse are always disjoint (X * X’ = 0) and (X + X’ = 1) because the values are either zero or one. Fuzzy logic membership functions can be either disjoint or non-disjoint. Example of a fuzzy non-linear and linear membership function X is shown (a) with its inverse membership function shown in (b). We first discuss a simplified logic with few literals
Fuzzy Intersection and Union
New Approach: Fuzzy to Multiple- valued Function Conversion and A/C Decomposition
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The result from the canonical form is the same as from the non-canonical form
Entire flow of our method Initial non-canonical expression Decomposition is based on finding patterns in this table
This way, the table is rewritten to the table from the next page docsity.com
Generalization of the Ashenhurst- Curtis decomposition model
This kind of tables known from Rough Sets, Decision Trees, etc Data Mining