Complexity Management in Fuzzy Systems: A Rule Base Compression ApproachDoing research is a great adventure As any adventure sometimes it is hard You may feel alone and with no idea where to go But if you have courage and press onwards You will eventually stand where no one has stood And see the world as no one has seen it There can be no better feeling than this! Adaptation from ‘Introduction to Research’, Tom Addis (2004) The idea about this book has been on the author’s mind for almost a decade but it was only about a couple of years ago when the underlying research process was actually started. The reason for this delay has been the insufficient spare time for research being a lecturer in a ‘new’ UK university where the emphasis is mainly on teaching. And maybe this book would have never been written if the author had not been presented with the chance of developing new teaching modules in fuzzy logic that have given him food for thought in a research related context and have helped him combine efficiently his teaching and research activities. The title of this book may sound too specialised but it has a much wider meaning. Fuzzy systems are any systems for modelling, simulation, control, prediction, diagnosis, decision making, pattern recognition, image processing, etc. which use fuzzy logic. Although fuzzy logic is an advanced extension of binary logic, the latter is still used predominantly today. |
Contents
| 7 | |
| 17 | |
Formal Presentation of Fuzzy Rule Based Systems 33 | 32 |
Formal Manipulation of Fuzzy Rule Based Systems | 65 |
Formal Manipulation with Special Rule Bases | 115 |
Formal Transformation of Fuzzy Rule Based Systems 153 | 152 |
Formal Transformation of Feedback Rule Bases | 185 |
Formal Simplification of Fuzzy Rule Based Systems | 269 |
Conclusion | 341 |
Index | 349 |
Other editions - View all
Complexity Management in Fuzzy Systems: A Rule Base Compression Approach Alexander Gegov No preview available - 2007 |
Complexity Management in Fuzzy Systems: A Rule Base Compression Approach Alexander Gegov No preview available - 2009 |
Complexity Management in Fuzzy Systems: A Rule Base Compression Approach Alexander Gegov No preview available - 2010 |
Common terms and phrases
addition aggregation Algorithm appearance application associated binary relation block Boolean matrix chapter column complexity consistent context corresponding Definition denoted described element equal Equation equivalent MRB system Example exhaustive FB function following Boolean following matrix formal fuzzy membership function fuzzy rule base fuzzy system horizontal identity incomplete initial input Inputs/Outputs 11 12 integer table introducing layer 1 layer layer 1 level level 1 o1 level/layer layer linguistic values manipulation maplets mapped matrix and binary merging monotonic non-exhaustive non-monotonic non-zero operand matrix operand rule bases operations output output-input interconnections parameters particular positions presented product matrix product rule base properties quantitative complexity RB RB RBF1 RBF2 reduced replaces represented respectively result rule base RB sequence shown shows single splitting standing step structure system are given techniques transformed unchanged usually vertical whereas whereby