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Group Method of Data Handling (GMDH) is a typical inductive modeling method built on the principles of self-organization. Since its introduction, inductive modeling has been developed and applied to complex systems in areas like prediction, modeling, clusterization, system identification, as well as data mining and knowledge extraction technologies, to several fields including social science, science, engineering, and medicine. This book makes error-free codes available to end-users so that these codes can be used to understand the implementation of GMDH, and then create opportunities to further develop the variants of GMDH algorithms. C-language has been chosen because it is a basic language commonly taught in the first year in computer programming courses in most universities and colleges, and the compiled versions could be used for more meaningful practical applications where security is necessary. Contents:Introduction (Godfrey C Onwubolu)GMDH Multilayered Iterative Algorithm (MIA) (Godfrey C Onwubolu)GMDH Multilayered Algorithm Using Prior Information (Alexandr Kiryanov)Combinatorial (COMBI) Algorithm (Oleksiy Koshulko, Anatoliy Koshulko and Godfrey C Onwubolu)GMDH Harmonic Algorithm (Godfrey C Onwubolu)GMDH-Based Modified Polynomial Neural Network Algorithm (Alexander Tyryshkin, Anatoliy Andrakhanov and Andrey Orlov)GMDH-Clustering (Lyudmyla Sarycheva and Alexander Sarychev)Multiagent Clustering Algorithm (Oleksii Oliinyk, Sergey Subbotin and Andrii Oliinyk)Analogue Complexing Algorithm (Dmytro Zubov)GMDH-Type Neural Network and Genetic Algorithm (Saeed Fallahi, Meysam Shaverdi and Vahab Bashiri) Readership: Researchers, professionals, and senior undergraduate students in artificial intelligence, neural networks, decision sciences, and innovation technology. Key Features:No other book in the market makes error-free codes so readily available to the publicClearly presents the main variants of GMDH and supporting codes for users to understand the concepts involved, apply them, and build on the available codesContributors are world-renowned researchers in GMDHKeywords:GMDH;Inductive Modeling;MIA;COMBI;PNN;GMDH-Analog Complexing
Group method of data handling (GMDH) is a typical inductive modeling method built on the principles of self-organization. Since its introduction, inductive modelling has been developed to support complex systems in prediction, clusterization, system identification, as well as data mining and knowledge extraction technologies in social science, science, engineering, and medicine. This is the first book to explore GMDH using MATLAB (matrix laboratory) language. Readers will learn how to implement GMDH in MATLAB as a method of dealing with big data analytics. Error-free source codes in MATLAB have been included in supplementary material (accessible online) to assist users in their understanding in GMDH and to make it easy for users to further develop variations of GMDH algorithms. Contents:Basic/Standard GMDH:Introduction (Godfrey C Onwubolu)GMDH Multilayered Algorithm (Godfrey C Onwubolu)GMDH Multilayered Algorithm in MATLAB (Mohammed Abdalla Ayoub Mohammed)Hybrid GMDH System:GMDH-Based Polynomial Neural Network Algorithm in MATLAB (Elaine Inácio Bueno, Iraci Martinez Pereira and Antonio Teixeira e Silva)Designing GMDH Model Using Modified Levenberg Marquardt Technique in Matlab (Maryam Pournasir Roudbaneh)Group Method of Data Handing Using Discrete Differential Evolution in Matlab (Donald Davendra, Godfrey Onwubolu and Ivan Zelinka) Readership: Professionals and students interested in data mining and analytics.
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