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Hybrid Self-Organizing Modeling Systems

Godfrey C. Onwubolu

Published by Springer-Verlag Gmbh Jun 2009, 2009
ISBN 10: 3642015298 / ISBN 13: 9783642015298
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Title: Hybrid Self-Organizing Modeling Systems

Publisher: Springer-Verlag Gmbh Jun 2009

Publication Date: 2009

Binding: Buch

Book Condition: Neu


Neuware - Models form the basis of any decision. They are used in di erent context and for di erent purposes: for identi cation, prediction, classi cation, or control of complex systems. Modeling is done theory-driven by logical-mathematical methods or data-driven based on observational data of the system and some algorithm or software for analyzing this data. Today, this approach is s- marized as Data Mining. There are many Data Mining algorithms known like Arti cial Neural N- works, Bayesian Networks, Decision Trees, Support Vector Machines. This book focuses on another method: the Group Method of Data Handling. - thoughthismethodologyhasnotyetbeenwellrecognizedintheinternational science community asa verypowerfulmathematicalmodeling andknowledge extraction technology, it has a long history. Developed in 1968bythe Ukrainianscientist A.G. Ivakhnenko it combines the black-box approach and the connectionism of Arti cial Neural Networks with well-proven Statistical Learning methods and with more behavior- justi ed elements of inductive self-organization.Over the past 40 years it has been improving and evolving, rst by works in the eld of what was known in the U.S.A. as Adaptive Learning Networks in the 1970s and 1980s and later by signi cantcontributions from scientists from Japan,China, Ukraine, Germany. Many papers and books have been published on this modeling technology, the vast majority of them in Ukrainian and Russian language. 280 pp. Englisch. Bookseller Inventory # 9783642015298

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Synopsis: The Group Method of Data Handling (GMDH) is a typical inductive modeling method that is built on principles of self-organization for modeling complex systems. However, it is known to often under-perform on non-parametric regression tasks, while time series modeling GMDH exhibits a tendency to find very complex polynomials that cannot model well future, unseen oscillations of the series. In order to alleviate these problems, GMDH has been recently hybridized with some computational intelligence (CI) techniques resulting in more robust and flexible hybrid intelligent systems for solving complex, real-world problems. The central theme of this book is to present in a very clear manner hybrids of some computational intelligence techniques and GMDH approach. The hybrids discussed in the book include GP-GMDH (Genetic Programming-GMDH) algorithm, GA-GMDH (Genetic Algorithm-GMDH) algorithm, DE-GMDH (Differential Evolution-GMDH) algorithm, and PSO-GMDH (Particle Swarm Optimization) algorithm. Also included is the description of the recently introduced GAME (Group Adaptive Models Evolution algorithm. The hybrid character of models and their self-organizing ability give these hybrid self-organizing modeling systems an advantage over standard data mining models. The modeling and data mining solutions of several real-life problems in the areas of engineering, bioinformatics, finance, and economics are presented in the chapters. The book will benefit amongst others, people who are working in the areas of neural networks, machine learning, artificial intelligence, complex system modeling and analysis, and optimization.

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