This quite simply superb book focuses on various techniques of computational intelligence, both single ones and those which form hybrid methods. These techniques are today commonly applied to issues of artificial intelligence. The book presents methods of knowledge representation using different techniques, namely the rough sets, type-1 fuzzy sets and type-2 fuzzy sets. Next up, various neural network architectures are presented and their learning algorithms are derived. Then, the family of evolutionary algorithms is discussed, including connections between these techniques and neural networks and fuzzy systems. Finally, various methods of data partitioning and algorithms of automatic data clustering are given and new neuro-fuzzy architectures are studied and compared.
|Publisher:||Springer Berlin Heidelberg|
|Edition description:||Softcover reprint of hardcover 1st ed. 2008|
|Product dimensions:||6.10(w) x 9.25(h) x 0.36(d)|
Table of ContentsSelected issues of artificial intelligence.- Methods of knowledge representation using rough sets.- Methods of knowledge representation using type-1 fuzzy sets.- Methods of knowledge representation using type-2 fuzzy sets.- Neural networks and their learning algorithms.- Evolutionary algorithms.- Data clustering methods.- Neuro-fuzzy systems of Mamdani, logical and Takagi-Sugeno type.- Flexible neuro-fuzzy systems.
Most Helpful Customer Reviews
Treatment of type-2 logic and rough sets particularly inviting.
Usable treatment of Neural networks with minimum Mathematics.
(Irritant:At two place derivation of formulas are left incomplete).
Excellent coverage of evolutionary techniques with enough rigor.
Clustering: Details are skipped, but still usable.