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Books in Fuzzy logic

Encyclopedia of Multi-Attribute Decision Making (MADM)
1st Edition
August 1, 2026
Gholamreza Haseli + 2 more
English
Encyclopedia of Multi-Attribute Decision Making (MADM) presents current methods in MADM in a simple way, including Sections on Weighting Methods, Extensions for the MADM Methods, Ranking Methods, and Outranking Methods. Each method chapter presents two numerical examples for each method, one simple example with less than six criteria and six alternatives, and one complex example with six or more criteria and six or more alternatives. In addition, most chapters are written by the original developers of the method, ensuring insight into and practical application of MADM. The book is also filled with over 200 full-color figures that illustrate methods and applications.The book, in one volume, demystifies the complex world of MADM, blending theoretical concepts with hands-on practices and case studies. It bridges the gap between theory and practical implementation, providing clear and practical understanding of the key principles and techniques essential for harnessing the power of MADM.
Emerging Fuzzy Intelligent Systems for Smart Healthcare Management
Applications of Disc q-Rung Orthopair Fuzzy Sets
1st Edition
April 10, 2025
Shahzaib Ashraf + 3 more
English
Emerging Fuzzy Intelligent Systems for Smart Healthcare Management: Applications of Disc q-Rung Orthopair Fuzzy Sets presents comprehensive methodological frameworks and the latest empirical research findings concerning disc q-rung orthopair fuzzy operators, with a specific focus on their applications in smart technologies for healthcare management. The book solves a crucial problem by offering readers an invaluable opportunity to conduct a comparative analysis, contrasting the proposed methods with their existing knowledge base. Disc q-Rung Orthopair sets, being the generalization of q-Rung Orthopair fuzzy sets, which are, in turn, the generalization of Pythagorean fuzzy sets, extend the capabilities of handling uncertainty beyond conventional fuzzy sets.The authors strive to narrow the knowledge gap by clarifying the practical applications of disc q-rung orthopair fuzzy logic. In addition, it explores an enhanced version of q-Rung Orthopair Fuzzy Sets, specifically focusing on Disc q-Rung Orthopair Fuzzy Sets, introducing various types of operators. These operators play a crucial role in solving decision-making and optimization problems. A notable contribution is the development of a hybrid operator, termed as the Disc q-Rung Orthopair Fuzzy Hybrid Weighted Averaging/Geometric (D-qROFHWA/G) operator.
Computational Intelligence in Sustainable Computing and Optimization
Trends and Applications
1st Edition
October 8, 2024
Balamurugan Balusamy + 4 more
English
Computational Intelligence in Sustainable Computing and Optimization: Trends and Applications focuses on developing and evolving advanced computational intelligence algorithms for the analysis of data involved in applications, such as agriculture, biomedical systems, bioinformatics, business intelligence, economics, disaster management, e-learning, education management, financial management, and environmental policies. The book presents research in sustainable computing and optimization, combining methods from engineering, mathematics, artificial intelligence, and computer science to optimize environmental resourcesComputation... intelligence in the field of sustainable computing combines computer science and engineering in applications ranging from Internet of Things (IoT), information security systems, smart storage, cloud computing, intelligent transport management, cognitive and bio-inspired computing, and management science. In addition, data intelligence techniques play a critical role in sustainable computing. Recent advances in data management, data modeling, data analysis, and artificial intelligence are finding applications in energy networks and thus making our environment more sustainable.
Computational Intelligence Applications for Text and Sentiment Data Analysis
1st Edition
July 14, 2023
Dipankar Das + 3 more
English
Computational Intelligence Applications for Text and Sentiment Data Analysis explores the most recent advances in text information processing and data analysis technologies, specifically focusing on sentiment analysis from multifaceted data. The book investigates a wide range of challenges involved in the accurate analysis of online sentiments, including how to i) identify subjective information from text, i.e., exclusion of ‘neutral’ or ‘factual’ comments that do not carry sentiment information, ii) identify sentiment polarity, and iii) domain dependency. Spam and fake news detection, short abbreviation, sarcasm, word negation, and a lot of word ambiguity are also explored.Further chapters look at the difficult process of extracting sentiment from different multimodal information (audio, video and text), semantic concepts. In each chapter, the book's authors explore how computational intelligence (CI) techniques, such as deep learning, convolutional neural network, fuzzy and rough set, global optimizers, and hybrid machine learning techniques play an important role in solving the inherent problems of sentiment analysis applications.
Uncertainty in Data Envelopment Analysis
Fuzzy and Belief Degree-Based Uncertainties
1st Edition
May 19, 2023
Farhad Hosseinzadeh Lotfi + 4 more
English
Classical data envelopment analysis (DEA) models use crisp data to measure the inputs and outputs of a given system. In cases such as manufacturing systems, production processes, service systems, etc., the inputs and outputs may be complex and difficult to measure with classical DEA models. Crisp input and output data are fundamentally indispensable in the conventional DEA models. If these models contain complex uncertain data, then they will become more important and practical for decision makers.Uncertainty in Data Envelopment Analysis introduces methods to investigate uncertain data in DEA models, providing a deeper look into two types of uncertain DEA methods, fuzzy DEA and belief degree-based uncertainty DEA, which are based on uncertain measures. These models aim to solve problems encountered by classical data analysis in cases where the inputs and outputs of systems and processes are volatile and complex, making measurement difficult.
Soft Numerical Computing in Uncertain Dynamic Systems
1st Edition
August 19, 2020
Tofigh Allahviranloo + 1 more
English
Soft Numerical Computing in Uncertain Dynamic Systems is intended for system specialists interested in dynamic systems that operate at different time scales. The book discusses several types of errors and their propagation, covering numerical methods—including convergence and consistence properties and characteristics—and proving of related theorems within the setting of soft computing. Several types of uncertainty representation like interval, fuzzy, type 2 fuzzy, granular, and combined uncertain sets are discussed in detail. The book can be used by engineering students in control and finite element fields, as well as all engineering, applied mathematics, economics, and computer science students. One of the important topics in applied science is dynamic systems and their applications. The authors develop these models and deliver solutions with the aid of numerical methods. Since they are inherently uncertain, soft computations are of high relevance here. This is the reason behind investigating soft numerical computing in dynamic systems. If these systems are involved with complex-uncertain data, they will be more practical and important. Real-life problems work with this type of data and most of them cannot be solved exactly and easily—sometimes they are impossible to solve. Clearly, all the numerical methods need to consider error of approximation. Other important applied topics involving uncertain dynamic systems include image processing and pattern recognition, which can benefit from uncertain dynamic systems as well. In fact, the main objective is to determine the coefficients of a matrix that acts as the frame in the image. One of the effective methods exhibiting high accuracy is to use finite differences to fill the cells of the matrix.
Diffuse Algorithms for Neural and Neuro-Fuzzy Networks
With Applications in Control Engineering and Signal Processing
1st Edition
February 10, 2017
Boris.A Skorohod
English
Diffuse Algorithms for Neural and Neuro-Fuzzy Networks: With Applications in Control Engineering and Signal Processing presents new approaches to training neural and neuro-fuzzy networks. This book is divided into six chapters. Chapter 1 consists of plants models reviews, problems statements, and known results that are relevant to the subject matter of this book. Chapter 2 considers the RLS behavior on a finite interval. The theoretical results are illustrated by examples of solving problems of identification, control, and signal processing. Properties of the bias, the matrix of second-order moments and the normalized average squared error of the RLS algorithm on a finite time interval are studied in Chapter 3. Chapter 4 deals with the problem of multilayer neural and neuro-fuzzy networks training with simultaneous estimation of the hidden and output layers parameters. The theoretical results are illustrated with the examples of pattern recognition, identification of nonlinear static, and dynamic plants. Chapter 5 considers the estimation problem of the state and the parameters of the discrete dynamic plants in the absence of a priori statistical information about initial conditions or its incompletion. The Kalman filter and the extended Kalman filter diffuse analogues are obtained. Finally, Chapter 6 provides examples of the use of diffuse algorithms for solving problems in various engineering applications. This book is ideal for researchers and graduate students in control, signal processing, and machine learning.
Reasoning About Plans
1st Edition
June 28, 2014
James Allen + 3 more
English
This book presents four contributions to planning research within an integrated framework. James Allen offers a survey of his research in the field of temporal reasoning, and then describes a planning system formalized and implemented directly as an inference process in the temporal logic. Starting from the same logic, Henry Kautz develops the first formal specification of the plan recognition process and develops a powerful family of algorithms for plan recognition in complex situations. Richard Pelavin then extends the temporal logic with model operators that allow the representation to support reasoning about complex planning situations involving simultaneous interacting actions, and interaction with external events. Finally, Josh Tenenberg introduces two different formalisms of abstraction in planning systems and explores the properties of these abstraction techniques in depth.
Fuzzy Logic and the Semantic Web
1st Edition
Volume 1
February 20, 2006
Elie Sanchez
English
These are exciting times in the fields of Fuzzy Logic and the Semantic Web, and this book will add to the excitement, as it is the first volume to focus on the growing connections between these two fields. This book is expected to be a valuable aid to anyone considering the application of Fuzzy Logic to the Semantic Web, because it contains a number of detailed accounts of these combined fields, written by leading authors in several countries. The Fuzzy Logic field has been maturing for forty years. These years have witnessed a tremendous growth in the number and variety of applications, with a real-world impact across a wide variety of domains with humanlike behavior and reasoning. And we believe that in the coming years, the Semantic Web will be major field of applications of Fuzzy Logic. This book, the first in the new series Capturing Intelligence, shows the positive role Fuzzy Logic, and more generally Soft Computing, can play in the development of the Semantic Web, filling a gap and facing a new challenge. It covers concepts, tools, techniques and applications exhibiting the usefulness, and the necessity, for using Fuzzy Logic in the Semantic Web. It finally opens the road to new systems with a high Web IQ. Most of today's Web content is suitable for human consumption. The Semantic Web is presented as an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. For example, within the Semantic Web, computers will understand the meaning of semantic data on a web page by following links to specified ontologies. But while the Semantic Web vision and research attracts attention, as long as it will be used two-valued-based logical methods no progress will be expected in handling ill-structured, uncertain or imprecise information encountered in real world knowledge. Fuzzy Logic and associated concepts and techniques (more generally, Soft Computing), has certainly a positive role to play in the development of the Semantic Web. Fuzzy Logic will not supposed to be the basis for the Semantic Web but its related concepts and techniques will certainly reinforce the systems classically developed within W3C. In fact, Fuzzy Logic cannot be ignored in order to bridge the gap between human-understandable soft logic and machine-readable hard logic. None of the usual logical requirements can be guaranteed: there is no centrally defined format for data, no guarantee of truth for assertions made, no guarantee of consistency. To support these arguments, this book shows how components of the Semantic Web (like XML, RDF, Description Logics, Conceptual Graphs, Ontologies) can be covered, with in each case a Fuzzy Logic focus.
An Ontological and Epistemological Perspective of Fuzzy Set Theory
1st Edition
November 15, 2005
I. Burhan Türksen
English
Fuzzy set and logic theory suggest that all natural language linguistic expressions are imprecise and must be assessed as a matter of degree. But in general membership degree is an imprecise notion which requires that Type 2 membership degrees be considered in most applications related to human decision making schemas. Even if the membership functions are restricted to be Type1, their combinations generate an interval – valued Type 2 membership. This is part of the general result that Classical equivalences breakdown in Fuzzy theory. Thus all classical formulas must be reassessed with an upper and lower expression that are generated by the breakdown of classical formulas.Key features:- Ontological grounding- Epistemological justification- Measurement of Membership- Breakdown of equivalences- FDCF is not equivalent to FCCF- Fuzzy Beliefs- Meta-Linguistic axioms