Описание: This book offers a new, theoretical approach to information dynamics, i.e., information processing in complex dynamical systems. The presentation establishes a consistent theoretical framework for the problem of discovering knowledge behind empirical, dynamical data and addresses applications in information processing and coding in dynamical systems. This will be an essential reference for those in neural computing, information theory, nonlinear dynamics and complex systems modeling.
Описание: Information fusion techniques and aggregation operators produce the most comprehensive, specific datum about an entity using data supplied from different sources, thus enabling us to reduce noise, increase accuracy, summarize and extract information, and make decisions. These techniques are applied in fields such as economics, biology and education, while in computer science they are particularly used in fields such as knowledge-based systems, robotics, and data mining.This book covers the underlying science and application issues related to aggregation operators, focusing on tools used in practical applications that involve numerical information. Starting with detailed introductions to information fusion and integration, measurement and probability theory, fuzzy sets, and functional equations, the authors then cover the following topics in detail: synthesis of judgements, fuzzy measures, weighted means and fuzzy integrals, indices and evaluation methods, model selection, and parameter extraction. The methods are illustrated with representative examples throughout, and there are extensive bibliographies and reading suggestions.The book is intended for graduate students, researchers, and practitioners such as engineers, computer scientists, statisticians and economists who use decision models and aggregation operators. The reader is assumed to have a nonspecialized background in mathematics.
Описание: One of the great challenges in flexible production and supply chains is the availability of necessary information at any time and any place. As a result of increasing dynamical and structural complexity of structures and processes in production it is often impossible to make all necessary information available to a central instance in real time and to perform appropriate measures of control in terms of a defined target system. A fast and flexible adaptation to changing basic conditions ought to be achieved by establishing autonomous logistics processes.In this context several fundamental questions concerning autonomous cooperating logistics processes were investigated:The identification problem: What are autonomous logistics processes and how do they differ from conventionally managed processes?The description problem: Which changes will autonomy cause in order processing?One of the first results is a definition for the term autonomy for applications in engineering science. The constituent characteristics of this definition were considered within the development of the catalogue of criteria in order to describe autonomous logistic processes. Regarding the modelling of autonomous processes, first requirements for modelling methods were specified. To validate the research results, a production-logistic shop-floor scenario and a practical scenario based on the real business processes of an automobile terminal were developed. Simulation studies concerning autonomously controlled allocation of parking areas document comprehensive opportunities for improvement.
Автор: Konishi Sadanori, Kitagawa Genshiro Название: Information Criteria and Statistical Modeling ISBN: 0387718869 ISBN-13(EAN): 9780387718866 Издательство: Springer Рейтинг: Цена: 11219 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The Akaike information criterion (AIC) derived as an estimator of the Kullback-Leibler information discrepancy provides a useful tool for evaluating statistical models, and numerous successful applications of the AIC have been reported in various fields of natural sciences, social sciences and engineering. One of the main objectives of this book is to provide comprehensive explanations of the concepts and derivations of the AIC and related criteria, including SchwarzвЂ™s Bayesian information criterion (BIC), together with a wide range of practical examples of model selection and evaluation criteria. A secondary objective is to provide a theoretical basis for the analysis and extension of information criteria via a statistical functional approach. A generalized information criterion (GIC) and a bootstrap information criterion are presented, which provide unified tools for modeling and model evaluation for a diverse range of models, including various types of nonlinear models and model estimation procedures such as robust estimation, the maximum penalized likelihood method and a Bayesian approach.
Описание: This book is a crystallization of the authors' work over the last twenty-five years. The book covers the latest advances in grey information and systems research, providing a state-of-the-art overview of this important field.Covering the theoretical foundation, fundamental methods and main topics in grey information and systems research, this book includes all the elementary concepts: basic principles, grey numbers and their operations, grey equations and matrices, operators of sequences and generations of grey sequences, grey incidence analysis, grey clusters and grey statistical evaluations, grey systems modeling, grey combined models, grey prediction, grey decisions, grey programming, grey input and output and grey controls, etc.The book will be of interest to advanced students and researchers in a wide range of fields including information and systems sciences and management sciences, and to those working in applied areas such as geo-science, engineering, agriculture, medicine, biosciences and others.
Автор: Niskanen Vesa A. Название: Soft Computing Methods in Human Sciences ISBN: 3540004661 ISBN-13(EAN): 9783540004660 Издательство: Springer Рейтинг: Цена: 15894 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book considers Soft Computing methods and their applications in the human sciences, such as the social and the behavioral sciences. Soft Computing methods - including fuzzy systems, neural networks, evolutionary computing and probabilistic reasoning - are state-of-the-art methods in theory formation and model construction. The powerful application areas of these methods in the human sciences are demonstrated, including the replacement of statistical models by simpler numerical or linguistic Soft Computing models and the use of computer simulations with approximate and linguistic constituents. "Dr. Niskanens work opens new vistas in application of soft computing, fuzzy logic and fuzzy set theory to the human sciences. This book is likely to be viewed in retrospect as a landmark in its field" - Lotfi A. Zadeh, Berkeley.
Описание: Agent-based modelling on a computer appears to have a special role to play in the development of social science. It offers a means of discovering general and applicable social theory, and grounding it in precise assumptions and derivations, whilst addressing those elements of individual cognition that are central to human society. However, there are important questions to be asked and difficulties to overcome in achieving this potential. What differentiates agent-based modelling from traditional computer modelling? Which model types should be used under which circumstances? If it is appropriate to use a complex model, how can it be validated? Is social simulation research to adopt a realist epistemology, or can it operate within a social constructionist framework? What are the sociological concepts of norms and norm processing that could either be used for planned implementation or for identifying equivalents of social norms among co-operative agents? Can sustainability be achieved more easily in a hierarchical agent society than in a society of isolated agents? What examples are there of hybrid forms of interaction between humans and artificial agents? These are some of the sociological questions that are addressed.
Автор: Torra VicenГ§ Название: Information Fusion in Data Mining ISBN: 3540006761 ISBN-13(EAN): 9783540006763 Издательство: Springer Рейтинг: Цена: 24217 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Information fusion is becoming a major requirement in data mining and knowledge discovery in databases. This book presents some recent fusion techniques that are currently in use in data mining, as well as data mining applications that use information fusion. Special focus of the book is on information fusion in preprocessing, model building and information extraction with various applications.
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