Whereas computer systems can easily handle even complicated and nonlinear mathematical models, human information processing is mainly based on linguistic knowledge. So the main advantage of using linguistic terms even with vague ranges is the intuitive interpretability of linguistic rules. Ishibuchi and his coauthors explain how classification and modeling can be handled in a human-understandable manner. They design a framework that can extract linguistic knowledge from numerical data by first identifying linguistic terms, then combining these terms into linguistic rules, and finally constructing a rule set from these linguistic rules. They combine their approach with state-of-the-art soft computing techniques such as multi-objective genetic algorithms, genetics-based machine learning, and fuzzified neural networks. Finally they demonstrate the usability of the combined techniques with various simulation results. In this largely self-contained volume, students specializing in soft computing will appreciate the detailed presentation, carefully discussed algorithms, and the many simulation experiments, while researchers will find a wealth of new design schemes, thorough analysis, and inspiring new research.
|Publisher:||Springer Berlin Heidelberg|
|Series:||Advanced Information Processing Series|
|Edition description:||Softcover reprint of hardcover 1st ed. 2005|
|Product dimensions:||6.10(w) x 9.00(h) x 0.90(d)|
Table of Contents
Linguistic Information Granules.- Pattern Classification with Linguistic Rules.- Learning of Linguistic Rules.- Input Selection and Rule Selection.- Genetics-Based Machine Learning.- Multi-Objective Design of Linguistic Models.- Comparison of Linguistic Discretization with Interval Discretization.- Modeling with Linguistic Rules.- Design of Compact Linguistic Models.- Linguistic Rules with Consequent Real Numbers.- Handling of Linguistic Rules in Neural Networks.- Learning of Neural Networks from Linguistic Rules.- Linguistic Rule Extraction from Neural Networks.- Modeling of Fuzzy Input—Output Relations.