http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Fuzzy and Neuro-Fuzzy Intelligent Systems
Intelligence systems. We perfonn routine tasks on a daily basis, as for example: • recognition of faces of persons (also faces not seen for many years), • identification of dangerous situations during car driving, • deciding to buy or sell stock, • reading hand-written symbols, • discriminating between vines made from Sauvignon Blanc, Syrah or Merlot grapes, and others. Human experts carry out the following: • diagnosing diseases, • localizing faults in electronic circuits, • optimal moves in chess games. It is possible to design artificial systems to replace or "duplicate" the human expert. There are many possible definitions of intelligence systems. One of them is that: an intelligence system is a system able to make decisions that would be regarded as intelligent ifthey were observed in humans. Intelligence systems adapt themselves using some example situations (inputs of a system) and their correct decisions (system's output). The system after this learning phase can make decisions automatically for future situations. This system can also perfonn tasks difficult or impossible to do for humans, as for example: compression of signals and digital channel equalization.
1 Classical sets and fuzzy sets Basic definitions and terminology -- 1.1 Classical sets -- 1.2 Fuzzy sets -- 1.3 Operations on fuzzy sets -- 1.4 Classification of t-norms and t-conorms -- 1.5 De Morgan triple and other properties of t- and s-norms -- 1.6 Parameterized t-, s-norms and negations -- 1.7 Fuzzy relations -- 1.8 Cylindrical extension and projection of fuzzy sets -- 1.9 Extension principle -- 1.10 Linguistic variable -- 1.11 Summary -- Bibliographical notes -- 2 Approximate reasoning -- 2.1 Interpretation of fuzzy conditional statement -- 2.2 An approach to axiomatic definition of fuzzy implication -- 2.3 Compositional rule of inference -- 2.4 Fuzzy reasoning -- 2.5 Canonical fuzzy if-then rule -- 2.6 Aggregation operation -- 2.7 Approximate reasoning using a fuzzy rule base -- 2.8 Approximate reasoning with singletons -- 2.9 Fuzzifiers and defuzzifiers -- 2.10 Equivalence of approximate reasoning results using different interpretations of if-then rules -- 2.11 Numerical results -- 2.12 Summary -- Bibliographical notes -- 3 Artificial neural networks -- 3.1 Introduction -- 3.2 Artificial neural networks topologies -- 3.3 Learning in artificial neural networks -- 3.4 Back-propagation learning rule -- 3.5 Modifications of the classic back-propagation method -- 3.6 Optimization methods in neural networks learning -- 3.7 Networks with output linearly depending on parameters -- 3.8 Global optimization methods -- 3.9 Summary -- Bibliographical notes -- 4 Unsupervised learning Clustering methods -- 4.1 Introduction -- 4.2 Self-organizing feature map -- 4.3 Vector quantization and learning vector quantization -- 4.4 An overview of clustering methods -- 4.5 Fuzzy clustering methods -- 4.6 A possibilistic approach to clustering -- 4.7 New generalized weighted conditional fuzzy c-means -- 4.8 Fuzzy learning vector quantization -- 4.9 Cluster validity -- 4.10 Summary -- Bibliographical notes -- 5 Fuzzy systems -- 5.1 Introduction -- 5.2 The Mamdani fuzzy systems -- 5.3 The Takagi-Sugeno-Kang fuzzy systems -- 5.4 Fuzzy systems with parameterized consequents -- 5.5 Summary -- Bibliographical notes -- 6 Neuro-fuzzy systems -- 6.1 Introduction -- 6.2 Artificial neural network based fuzzy inference system -- 6.3 Classifier based on neuro-fuzzy system -- 6.4 ANNBFIS optimization using deterministic annealing -- 6.5 Further investigations of neuro-fuzzy systems -- 6.6 Summary -- Bibliographical notes -- Appendix A: Artificial neural network based fuzzy inference systema MATLAB implementation -- Appendix B: Proof of classifier learning convergence -- 7 Applications of artificial neural network based fuzzy inference system -- 7.1 Introduction -- 7.2 Application to chaotic time series prediction -- 7.3 Application to ECG signal compression -- 7.4 Application to Ripley's synthetic two-class data classification -- 7.5 Application to the recognition of diabetes in Pima Indians -- 7.6 Application to the iris problem -- 7.7 Application to Monk's problems -- 7.8 Application to system identification -- 7.9 Application to control -- 7.10 Application to channel equalization -- 7.11 Summary -- Biographical notes -- References -- List of notations and abbreviations.
Studies in Fuzziness and Soft Computing, 47