Overview
- First book to present a mathematical framework for neurofuzzy modeling using generalised linear neural networks Focus on practically applicable algorithms for data based modeling and time series problems as market forecasting
- Lots of benchmark examples to show the algorithms' efficiencies
- Includes supplementary material: sn.pub/extras
Part of the book series: Advanced Information Processing (AIP)
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Table of contents (10 chapters)
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Front Matter
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Back Matter
Keywords
About this book
In a world of almost permanent and rapidly increasing electronic data availability, techniques of filtering, compressing, and interpreting this data to transform it into valuable and easily comprehensible information is of utmost importance. One key topic in this area is the capability to deduce future system behavior from a given data input.
This book brings together for the first time the complete theory of data-based neurofuzzy modelling and the linguistic attributes of fuzzy logic in a single cohesive mathematical framework. After introducing the basic theory of data-based modelling, new concepts including extended additive and multiplicative submodels are developed and their extensions to state estimation and data fusion are derived. All these algorithms are illustrated with benchmark and real-life examples to demonstrate their efficiency.
Chris Harris and his group have carried out pioneering work which has tied together the fields of neural networks and linguistic rule-based algortihms. This book is aimed at researchers and scientists in time series modeling, empirical data modeling, knowledge discovery, data mining, and data fusion.
Reviews
From the reviews:
"This is an account of a major development by a research group in Southampton University on the extension of adaptive techniques to nonlinear and nonstationary environments. … There seems to be no doubt that this well-presented book is indispensable for anyone concerned with difficult nonlinear problems of control." (Alex M. Andrew, Robotica, Vol. 22, 2004)
"This book brings together for the first time the complete theory of data-based neurofuzzy modelling and the linguistic attributes of fuzzy logic in a single cohesive mathematical framework. … This book is aimed at researchers and scientists in time series modelling, empirical data modelling, knowledge discovery, data mining, and data fusion." (Nikolay Yakovlevich Tikhonenko, Zentralblatt MATH, Vol. 1005, 2003)
Authors and Affiliations
Bibliographic Information
Book Title: Adaptive Modelling, Estimation and Fusion from Data
Book Subtitle: A Neurofuzzy Approach
Authors: Chris Harris, Xia Hong, Qiang Gan
Series Title: Advanced Information Processing
DOI: https://doi.org/10.1007/978-3-642-18242-6
Publisher: Springer Berlin, Heidelberg
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eBook Packages: Springer Book Archive
Copyright Information: Springer-Verlag Berlin Heidelberg 2002
Hardcover ISBN: 978-3-540-42686-8Published: 13 May 2002
Softcover ISBN: 978-3-642-62119-2Published: 21 September 2012
eBook ISBN: 978-3-642-18242-6Published: 05 October 2012
Edition Number: 1
Number of Pages: XVI, 323
Topics: Artificial Intelligence, Pattern Recognition, Theory of Computation, Mathematics of Computing, Information Storage and Retrieval, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences