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Books in Fuzzy logic

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1-10 of 11 results in All results

Computational Intelligence in Sustainable Computing and Optimization

  • 1st Edition
  • October 1, 2024
  • Balamurugan Balusamy + 4 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 3 7 2 4 - 9
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 3 7 2 5 - 6
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 resourcesComputational 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
  • Paperback
    9 7 8 - 0 - 3 2 3 - 9 0 5 3 5 - 0
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 0 6 3 7 - 1
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

  • 1st Edition
  • May 19, 2023
  • Farhad Hosseinzadeh Lotfi + 4 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 9 9 4 4 4 - 6
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 9 4 4 5 - 3
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
  • Paperback
    9 7 8 - 0 - 1 2 - 8 2 2 8 5 5 - 5
  • eBook
    9 7 8 - 0 - 1 2 - 8 2 2 9 9 4 - 1
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

  • 1st Edition
  • February 10, 2017
  • Boris.A Skorohod
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 1 2 6 0 9 - 7
  • eBook
    9 7 8 - 0 - 1 2 - 8 1 2 6 1 0 - 3
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.

Fuzzy Neural Networks for Real Time Control Applications

  • 1st Edition
  • September 17, 2015
  • Erdal Kayacan + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 2 6 8 7 - 8
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 2 7 0 3 - 5
AN INDISPENSABLE RESOURCE FOR ALL THOSE WHO DESIGN AND IMPLEMENT TYPE-1 AND TYPE-2 FUZZY NEURAL NETWORKS IN REAL TIME SYSTEMS   Delve into the type-2 fuzzy logic systems and become engrossed in the parameter update algorithms for type-1 and type-2 fuzzy neural networks and their stability analysis with this book! Not only does this book stand apart from others in its focus but also in its application-based presentation style. Prepared in a way that can be easily understood by those who are experienced and inexperienced in this field. Readers can benefit from the computer source codes for both identification and control purposes which are given at the end of the book. A clear and an in-depth examination has been made of all the necessary mathematical foundations, type-1 and type-2 fuzzy neural network structures and their learning algorithms as well as their stability analysis. You will find that each chapter is devoted to a different learning algorithm for the tuning of type-1 and type-2 fuzzy neural networks; some of which are: • Gradient descent • Levenberg-Marquardt • Extended Kalman filter In addition to the aforementioned conventional learning methods above, number of novel sliding mode control theory-based learning algorithms, which are simpler and have closed forms, and their stability analysis have been proposed. Furthermore, hybrid methods consisting of particle swarm optimization and sliding mode control theory-based algorithms have also been introduced. The potential readers of this book are expected to be the undergraduate and graduate students, engineers, mathematicians and computer scientists. Not only can this book be used as a reference source for a scientist who is interested in fuzzy neural networks and their real-time implementations but also as a course book of fuzzy neural networks or artificial intelligence in master or doctorate university studies. We hope that this book will serve its main purpose successfully.

Reasoning About Plans

  • 1st Edition
  • June 28, 2014
  • James Allen + 3 more
  • English
  • eBook
    9 7 8 - 1 - 4 8 3 2 - 9 5 9 6 - 1
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
  • eBook
    9 7 8 - 0 - 0 8 - 0 4 6 0 4 8 - 2
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
  • eBook
    9 7 8 - 0 - 0 8 - 0 5 2 5 7 1 - 6
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

Fuzzy Logic in Geology

  • 1st Edition
  • October 20, 2003
  • Robert V. Demicco + 1 more
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 0 5 2 1 8 9 - 3
What is fuzzy logic?--a system of concepts and methods for exploring modes of reasoning that are approximate rather than exact. While the engineering community has appreciated the advances in understanding using fuzzy logic for quite some time, fuzzy logic's impact in non-engineering disciplines is only now being recognized. The authors of Fuzzy Logic in Geology attend to this growing interest in the subject and introduce the use of fuzzy set theory in a style geoscientists can understand. This is followed by individual chapters on topics relevant to earth scientists: sediment modeling, fracture detection, reservoir characterization, clustering in geophysical data analysis, ground water movement, and time series analysis.