Uncertain Logics, Variables and Systems

Uncertain Logics, Variables and Systems PDF Author: Z. Bubnicki
Publisher: Springer
ISBN: 3540457941
Category : Technology & Engineering
Languages : en
Pages : 140

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Book Description
The ideas of uncertain variables based on uncertain logics have been introduced and developed for a wide class of uncertain systems. The purpose of this mo- graph is to present basic concepts, definitions and results concerning the uncertain variables and their applications to analysis and decision problems in uncertain systems described by traditional mathematical models and by knowledge rep- sentations. I hope that the book can be useful for graduate students, researchers and all readers working in the field of control and information science. Especially for those interested in the problems of uncertain decision support systems and unc- tain control systems. I wish to express my gratitude to my co-workers from the Institute of Control and Systems Engineering of Wroclaw University of Technology, who assisted in the preparation of the manuscript. My special thanks go to Dr L.Siwek for the valuable remarks and for his work concerning the formatting of the text.

Uncertain Logics, Variables and Systems

Uncertain Logics, Variables and Systems PDF Author: Z. Bubnicki
Publisher: Springer
ISBN: 3540457941
Category : Technology & Engineering
Languages : en
Pages : 140

Get Book Here

Book Description
The ideas of uncertain variables based on uncertain logics have been introduced and developed for a wide class of uncertain systems. The purpose of this mo- graph is to present basic concepts, definitions and results concerning the uncertain variables and their applications to analysis and decision problems in uncertain systems described by traditional mathematical models and by knowledge rep- sentations. I hope that the book can be useful for graduate students, researchers and all readers working in the field of control and information science. Especially for those interested in the problems of uncertain decision support systems and unc- tain control systems. I wish to express my gratitude to my co-workers from the Institute of Control and Systems Engineering of Wroclaw University of Technology, who assisted in the preparation of the manuscript. My special thanks go to Dr L.Siwek for the valuable remarks and for his work concerning the formatting of the text.

Modeling Uncertainty with Fuzzy Logic

Modeling Uncertainty with Fuzzy Logic PDF Author: Asli Celikyilmaz
Publisher: Springer
ISBN: 3540899243
Category : Computers
Languages : en
Pages : 443

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Book Description
The world we live in is pervaded with uncertainty and imprecision. Is it likely to rain this afternoon? Should I take an umbrella with me? Will I be able to find parking near the campus? Should I go by bus? Such simple questions are a c- mon occurrence in our daily lives. Less simple examples: What is the probability that the price of oil will rise sharply in the near future? Should I buy Chevron stock? What are the chances that a bailout of GM, Ford and Chrysler will not s- ceed? What will be the consequences? Note that the examples in question involve both uncertainty and imprecision. In the real world, this is the norm rather than exception. There is a deep-seated tradition in science of employing probability theory, and only probability theory, to deal with uncertainty and imprecision. The mon- oly of probability theory came to an end when fuzzy logic made its debut. H- ever, this is by no means a widely accepted view. The belief persists, especially within the probability community, that probability theory is all that is needed to deal with uncertainty. To quote a prominent Bayesian, Professor Dennis Lindley, “The only satisfactory description of uncertainty is probability.

Modern Control Theory

Modern Control Theory PDF Author: Zdzislaw Bubnicki
Publisher: Springer Science & Business Media
ISBN: 3540280871
Category : Technology & Engineering
Languages : en
Pages : 422

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Book Description
Well-written, practice-oriented textbook, and compact textbook Presents the contemporary state of the art of control theory and its applications Introduces traditional problems that are useful in the automatic control of technical processes, plus presents current issues of control Explains methods can be easily applied for the determination of the decision algorithms in computer control and management systems

Analysis and Decision Making in Uncertain Systems

Analysis and Decision Making in Uncertain Systems PDF Author: Zdzislaw Bubnicki
Publisher: Springer Science & Business Media
ISBN: 1447137604
Category : Technology & Engineering
Languages : en
Pages : 377

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Book Description
A unified and systematic description of analysis and decision problems within a wide class of uncertain systems, described by traditional mathematical methods and by relational knowledge representations. Prof. Bubnicki takes a unique approach to stability and stabilization of uncertain systems.

Neural Networks and Soft Computing

Neural Networks and Soft Computing PDF Author: Leszek Rutkowski
Publisher: Springer Science & Business Media
ISBN: 9783790800050
Category : Computers
Languages : en
Pages : 940

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Book Description
This volume presents new trends and developments in soft computing techniques. Topics include: neural networks, fuzzy systems, evolutionary computation, knowledge discovery, rough sets, and hybrid methods. It also covers various applications of soft computing techniques in economics, mechanics, medicine, automatics and image processing. The book contains contributions from internationally recognized scientists, such as Zadeh, Bubnicki, Pawlak, Amari, Batyrshin, Hirota, Koczy, Kosinski, Novák, S.-Y. Lee, Pedrycz, Raudys, Setiono, Sincak, Strumillo, Takagi, Usui, Wilamowski and Zurada. An excellent overview of soft computing methods and their applications.

Design of Logic-based Intelligent Systems

Design of Logic-based Intelligent Systems PDF Author: Klaus Truemper
Publisher: John Wiley & Sons
ISBN: 9780471484035
Category : Technology & Engineering
Languages : en
Pages : 368

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Book Description
Principles for constructing intelligent systems Design of Logic-based Intelligent Systems develops principles and methods for constructing intelligent systems for complex tasks that are readily done by humans but are difficult for machines. Current Artificial Intelligence (AI) approaches rely on various constructs and methods (production rules, neural nets, support vector machines, fuzzy logic, Bayesian networks, etc.). In contrast, this book uses an extension of propositional logic that treats all aspects of intelligent systems in a unified and mathematically compatible manner. Topics include: * Levels of thinking and logic * Special cases: expert systems and intelligent agents * Formulating and solving logic systems * Reasoning under uncertainty * Learning logic formulas from data * Nonmonotonic and incomplete reasoning * Question-and-answer processes * Intelligent systems that construct intelligent systems Design of Logic-based Intelligent Systems is both a handbook for the AI practitioner and a textbook for advanced undergraduate and graduate courses on intelligent systems. Included are more than forty algorithms, and numerous examples and exercises. The purchaser of the book may obtain an accompanying software package (Leibniz System) free of charge via the internet at leibnizsystem.com.

Modeling Uncertainty with Fuzzy Logic

Modeling Uncertainty with Fuzzy Logic PDF Author: Asli Celikyilmaz
Publisher: Springer Science & Business Media
ISBN: 3540899235
Category : Computers
Languages : en
Pages : 443

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Book Description
The world we live in is pervaded with uncertainty and imprecision. Is it likely to rain this afternoon? Should I take an umbrella with me? Will I be able to find parking near the campus? Should I go by bus? Such simple questions are a c- mon occurrence in our daily lives. Less simple examples: What is the probability that the price of oil will rise sharply in the near future? Should I buy Chevron stock? What are the chances that a bailout of GM, Ford and Chrysler will not s- ceed? What will be the consequences? Note that the examples in question involve both uncertainty and imprecision. In the real world, this is the norm rather than exception. There is a deep-seated tradition in science of employing probability theory, and only probability theory, to deal with uncertainty and imprecision. The mon- oly of probability theory came to an end when fuzzy logic made its debut. H- ever, this is by no means a widely accepted view. The belief persists, especially within the probability community, that probability theory is all that is needed to deal with uncertainty. To quote a prominent Bayesian, Professor Dennis Lindley, “The only satisfactory description of uncertainty is probability.

Uncertain Rule-Based Fuzzy Systems

Uncertain Rule-Based Fuzzy Systems PDF Author: Jerry M. Mendel
Publisher: Springer
ISBN: 3319513702
Category : Technology & Engineering
Languages : en
Pages : 701

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Book Description
The second edition of this textbook provides a fully updated approach to fuzzy sets and systems that can model uncertainty — i.e., “type-2” fuzzy sets and systems. The author demonstrates how to overcome the limitations of classical fuzzy sets and systems, enabling a wide range of applications from time-series forecasting to knowledge mining to control. In this new edition, a bottom-up approach is presented that begins by introducing classical (type-1) fuzzy sets and systems, and then explains how they can be modified to handle uncertainty. The author covers fuzzy rule-based systems – from type-1 to interval type-2 to general type-2 – in one volume. For hands-on experience, the book provides information on accessing MatLab and Java software to complement the content. The book features a full suite of classroom material.

Uncertainty Analysis in Engineering and Sciences: Fuzzy Logic, Statistics, and Neural Network Approach

Uncertainty Analysis in Engineering and Sciences: Fuzzy Logic, Statistics, and Neural Network Approach PDF Author: Bilal M. Ayyub
Publisher: Springer Science & Business Media
ISBN: 146155473X
Category : Computers
Languages : en
Pages : 376

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Book Description
Uncertainty has been of concern to engineers, managers and . scientists for many centuries. In management sciences there have existed definitions of uncertainty in a rather narrow sense since the beginning of this century. In engineering and uncertainty has for a long time been considered as in sciences, however, synonymous with random, stochastic, statistic, or probabilistic. Only since the early sixties views on uncertainty have ~ecome more heterogeneous and more tools to model uncertainty than statistics have been proposed by several scientists. The problem of modeling uncertainty adequately has become more important the more complex systems have become, the faster the scientific and engineering world develops, and the more important, but also more difficult, forecasting of future states of systems have become. The first question one should probably ask is whether uncertainty is a phenomenon, a feature of real world systems, a state of mind or a label for a situation in which a human being wants to make statements about phenomena, i. e. , reality, models, and theories, respectively. One cart also ask whether uncertainty is an objective fact or just a subjective impression which is closely related to individual persons. Whether uncertainty is an objective feature of physical real systems seems to be a philosophical question. This shall not be answered in this volume.

Probabilistic Reasoning in Intelligent Systems

Probabilistic Reasoning in Intelligent Systems PDF Author: Judea Pearl
Publisher: Elsevier
ISBN: 0080514898
Category : Computers
Languages : en
Pages : 573

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Book Description
Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertainty--and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition--in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information. Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.