Computational Intelligence in Theory and Practice

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.

Inhalt

Fuzzy Logic Theory: D. Dubois and H. Prade: A Fuzzy Set Approach to Case-Based Decision.- R. Hähnle: Proof Theory of Many-Valued Logic and Linear Optimization.- R. Mesiar: Triangular Norms - An Overview.- Rough Sets: E.E. Kerre: A First View on the Alternatives of Fuzzy Set Theory.- Z. Pawlak: Rough Sets and Their Applications.- H. Thiele: Generalizing the Explicit Concept of Rough Set on the Basis of Modal Logic.- Fuzzy Clustering: C. Borgelt, H. Timm and R. Kruse: Probabilistic Networks and Fuzzy Clustering as Generalizations of Naive Bayes Classifiers.- W. Pedrycz: Granular Computing in Fuzzy Modeling and Data Mining.- F. Jiminez and J. Verdegay: Evolutionary Computation and Mathematical Programming.- U. Bodenhofer and P. Klement: Genetic Optimization of Fuzzy Classification Systems - A Case Study.- Fuzzy Data Models and Bases: P. Bosc: Graduality and Databases: Some Contributions of Fuzzy Sets.- E. Damiani: Fuzzy Retrieval of Object-Oriented Software Components-. J. Kacprzyk and S. Zadrozny: Using Fuzzy Querying over the Internet to Browse through Information Resources.