http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing
An Evolutionary Approach for Neural Networks and Fuzzy Systems
This monograph describes new methods for intelligent pattern recognition using soft computing techniques including neural networks, fuzzy logic, and genetic algorithms. Hybrid intelligent systems that combine several soft computing techniques are needed due to the complexity of pattern recognition problems. Hybrid intelligent systems can have different architectures, which have an impact on the efficiency and accuracy of pattern recognition systems, to achieve the ultimate goal of pattern recognition. This book also shows results of the application of hybrid intelligent systems to real-world problems of face, fingerprint, and voice recognition. This monograph is intended to be a major reference for scientists and engineers applying new computational and mathematical tools to intelligent pattern recognition and can be also used as a textbook for graduate courses in soft computing, intelligent pattern recognition, computer vision, or applied artificial intelligence.
Introduction to Pattern Recognition with Intelligent Systems -- Type-1 Fuzzy Logic -- Type-2 Fuzzy Logic and Intuitionistic Fuzzy Logic -- Supervised Neural Networks -- Unsupervised Neural Networks -- Modular Neural Networks -- Evolutionary Computing for Architecture Optimization -- Clustering with Intelligent Techniques -- Face Recognition with Modular Neural Networks and Fuzzy Measures -- Fingerprint Recognition with Modular Neural Networks and Fuzzy Measures -- Voice Recognition with Neural Networks and Genetic Algorithms -- Human Recognition Using Face, Fingerprint and Voice.
Appl.Mathematics/Computational Methods of Engineering
Artificial Intelligence (incl. Robotics)
Studies in Fuzziness and Soft Computing, 172