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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.
Getting started. Forecasting methods. Evaluation. Comparing methods. Commencement.
Contains case studies from engineering and operations research Includes commented literature for each chapter
Thermal systems play an increasingly symbiotic role alongside mechanical systems in varied applications spanning materials processing, energy conversion, pollution, aerospace, and automobiles. Responding to the need for a flexible, yet systematic approach to designing thermal systems across such diverse fields, Design and Optimization of Thermal Systems, Second Edition provides hands-on guidance needed to solve practical and progressively complex design problems. This bookoffers a thorough examination of basic concepts and procedures for conceptual design, formulation, modeling, simulation, feasible design, and optimization. The chapters encompass traditional as well as emerging techniques, featuring timely and compelling examples to demonstrate the range of potential problems and available solutions that readers may apply to their own needs. Maintaining its emphasis on mathematical modeling and simulation techniques, this revised edition offers extended coverage on manufacturability, material selection, and sensitivity. It includes new material on genetic and gradient search methods and highlights significant trends such as knowledge-based design methodology. This edition also updates and enhances its coverage of important economic, safety, security, and environmental aspects and considerations.
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This highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software. In keeping with the previous edition, this book is about the art and science of data analysis and predictive modeling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for fitting nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. The reader will gain a keen understanding of predictive accuracy and the harm of categorizing continuous predictors or outcomes. This text realistically deals with model uncertainty and its effects on inference, to achieve "safe data mining." It also presents many graphical methods for communicating complex regression models to non-statisticians. Regression Modeling Strategies presents full-scale case studies of non-trivial datasets instead of over-simplified illustrations of each method. These case studies use freely available R functions that make the multiple imputation, model building, validation and interpretation tasks described in the book relatively easy to do. Most of the methods in this text apply to all regression models, but special emphasis is given to multiple regression using generalized least squares for longitudinal data, the binary logistic model, models for ordinal responses, parametric survival regression models and the Cox semi parametric survival model. A new emphasis is given to the robust analysis of continuous dependent variables using ordinal regression. As in the first edition, this text is intended for Masters' or Ph.D. level graduate students who have had a general introductory probability and statistics course and who are well versed in ordinary multiple regression and intermediate algebra. The book will also serve as a reference for data analysts and statistical methodologists, as it contains an up-to-date survey and bibliography of modern statistical modeling techniques. Examples used in the text mostly come from biomedical research, but the methods are applicable anywhere predictive models ("analytics") are useful, including economics, epidemiology, sociology, psychology, engineering and marketing.

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