Description
Book SynopsisThere are many situations in science and engineering where complex output data from a given system is used to formulate a model of how that system operates, or to simulate its response to different inputs. Applications include control, decision theory, and the emerging fields of bioinformatics.
Trade Review"To cope with real world uncertainties and provide a philosophical and practical guide...several methodologies are presented..." (SciTech Book News, Vol. 25, No. 4, December 2001)
"...certainly a book that should be in the library of any institution where research and advanced study in fuzzy systems are carried out." (Choice, Vol. 39, No. 7, March 2002)
"...well organized, easy to read, and self-contained.... I would recommend it to anyone interested in self-study of the basic ideas of fuzzy systems..." (International Journal of General Systems, Vol. 31, No. 6, 2002)
Table of ContentsPreface.
Acknowledgments.
Introduction.
System Analysis.
Uncertainty Techniques.
Learning from Data: System Identification.
Propositions as Subsets of the Data Space.
Fuzzy Systems and Identification.
Random-Set Modelling and Identification.
Certain Uncertainty.
Fuzzy Inference Engines.
Fuzzy Classification.
Fuzzy Control.
Fuzzy Mathematics.
Summary.
Appendices.
Index.
