Computational Intelligence in Theory and PracticeBernd Reusch, Karl-Heinz Temme Computational Intelligence with its roots in Fuzzy Logic, Neural Networks and Evolutionary Algorithms has become an important research and application field in computer science in the last decade. Methodologies from these areas and combinations of them enable users from engineering, business, medicine and many more branches to capture and process vague, incomplete, uncertain and imprecise data and knowledge. Many algorithms and tools have been developed to solve problems in the realms of high and low level control, information processing, diagnostics, decision support, classification, optimisation and many more. This book tries to show the impact and feedback between theory and applications of Computational Intelligence, highlighted on selected examples. |
Contents
1 | |
Optimization | 15 |
Triangular Norms An Overview | 34 |
R Mesiar 35 | 49 |
Rough Sets | 55 |
Z Pawlak | 73 |
Basis of Modal Logic | 92 |
Fuzzy Clustering | 126 |
Granular Computing in Fuzzy Modeling and Data | 139 |
Evolutionary Computation and Mathematical | 167 |
Genetic Optimization of Fuzzy Classification Systems | 183 |
Fuzzy Data Models and Bases | 201 |
Fuzzy Retrieval of ObjectOriented Software | 221 |
Using Fuzzy Querying over the Internet to Browse | 234 |
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Computational Intelligence in Theory and Practice Bernd Reusch,Karl-Heinz Temme No preview available - 2014 |
Common terms and phrases
algebra application approach Archimedean attributes binary Boolean Bosc browser complete lattice condition constraint context continuous t-norms crisp data mining database decision defined definition denoted Dubois element equivalence relation example flou set theory functional dependencies fuzzy clustering fuzzy logic fuzzy querying fuzzy relations fuzzy set theory fuzzy terms Genetic Algorithms Gödel granularity hence implication infimum information granules Intelligent interface iris Kacprzyk Knowledge Discovery linear linguistic quantifier LRS(W Lukasiewicz Łukasiewicz logic many-valued logic matching degree membership function methods MIP-representable naive Bayes classifiers networks nilpotent object obtained operators optimization parameters Pawlak pixel Prade probabilistic problem programming properties Proposition prototype respect rough set theory selection semantics semilattice server Sets and Systems signed formula Soft Computing solution subset t-norms T. Y. Lin T₁ techniques Theorem Trader triangular norm truth values upper approximation URS(W variable Workshop on Rough Zadeh