Download Full eBooks in PDF
Data Driven Modelling With Fuzzy Sets Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Data Driven Modelling With Fuzzy Sets book. This book definitely worth reading, it is an incredibly well-written.
Author : Said Broumi,D Nagarajan,Michael Gr Voskoglou,S A Edalatpanah
Publisher : Unknown
Page : 0 pages
File Size : 41,5 Mb
Release : 2024-07-17
Category : Computers
ISBN : 1032550104
This book presents recent theoretical advances and applications of fuzzy sets and their extensions to Science, Humanities and Education.
Author : Said Broumi,D. Nagarajan,Michael Gr. Voskoglou,S. A. Edalatpanah
Publisher : CRC Press
Page : 235 pages
File Size : 40,7 Mb
Release : 2024-07-03
Category : Computers
ISBN : 9781040041581
Fuzzy sets have long been employed to handle imprecise and uncertain information in the real world, but their limitations in dealing with incomplete and inconsistent data led to the emergence of neutrosophic sets. In this thought-provoking book, titled Data-Driven Modelling with Fuzzy Sets: A Neutrosophic Perspective, the authors delve into the theories and extensive applications of neutrosophic sets, ranging from neutrosophic graphs to single-valued trapezoidal neutrosophic sets and their practical implications in knowledge management, including student learning assessment, academic performance evaluation, and technical article screening. This comprehensive resource is intended to benefit mathematicians, physicists, computer experts, engineers, scholars, practitioners, and students seeking to deepen their understanding of neutrosophic sets and their practical applications in diverse fields. This book comprises 11 chapters that provide a thorough examination of neutrosophic set theory and its extensions. Each chapter presents valuable insights into various aspects of data-driven modeling with neutrosophic sets and explores their applications in different domains. The book covers a wide range of topics. The specific topics covered in the book include neutrosophic submodules, applications of neutrosophic sets, solutions to differential equations with neutrosophic uncertainty, cardinalities of neutrosophic sets, neutrosophic cylindrical coordinates, applications to graphs and climatic analysis, neutrosophic differential equation approaches to growth models, neutrosophic aggregation operators for decision making, and similarity measures for Fermatean neutrosophic sets. The diverse contributions from experts in the field, coupled with the constructive feedback from reviewers, ensure the book's high quality and relevance. This book presents a qualitative assessment of big data in the education sector using linguistic quadripartitioned single-valued neutrosophic soft sets showcases application of n-cylindrical fuzzy neutrosophic sets in education using neutrosophic affinity degree and neutrosophic similarity index covers scientific evaluation of student academic performance using single-valued neutrosophic Markov chain illustrates multi-granulation single-valued neutrosophic probabilistic rough sets for teamwork assessment examines estimation of distribution algorithms based on multiple-attribute group decision-making to evaluate teaching quality With its wealth of knowledge, this book aims to inspire further research and innovation in the field of neutrosophic sets and their extensions, providing a valuable resource for scholars, practitioners, and students alike.
Author : Said Broumi,D. Nagarajan,Michael Gr. Voskoglou,S. A. Edalatpanah
Publisher : CRC Press
Page : 348 pages
File Size : 41,6 Mb
Release : 2024-07-17
Category : Computers
ISBN : 9781040043066
Zadeh introduced in 1965 the theory of fuzzy sets, in which truth values are modelled by numbers in the unit interval [0, 1], for tackling mathematically the frequently appearing in everyday life partial truths. In a second stage, when membership functions were reinterpreted as possibility distributions, fuzzy sets were extensively used to embrace uncertainty modelling. Uncertainty is defined as the shortage of precise knowledge or complete information and possibility theory is devoted to the handling of incomplete information. Zadeh articulated the relationship between possibility and probability, noticing that what is probable must preliminarily be possible. Following the Zadeh’s fuzzy set, various generalizations (intuitionistic, neutrosophic, rough, soft sets, etc.) have been introduced enabling a more effective management of all types of the existing in real world uncertainty. This book presents recent theoretical advances and applications of fuzzy sets and their extensions to Science, Humanities and Education. This book: Presents a qualitative assessment of big data in the education sector using linguistic Quadri partitioned single valued neutrosophic soft sets. Showcases application of n-cylindrical fuzzy neutrosophic sets in education using neutrosophic affinity degree and neutrosophic similarity Index. Covers scientific evaluation of student academic performance using single value neutrosophic Markov chain. Illustrates multi-granulation single-valued neutrosophic probabilistic rough sets for teamwork assessment. Examines estimation of distribution algorithm based on multiple attribute group decision-making to evaluate teaching quality. It is primarily written for Senior undergraduate and graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science and engineering.
Author : Radu-Emil Precup,Raul-Cristian Roman,Ali Safaei
Publisher : CRC Press
Page : 402 pages
File Size : 40,6 Mb
Release : 2021-12-27
Category : Technology & Engineering
ISBN : 9781000519587
This book categorizes the wide area of data-driven model-free controllers, reveals the exact benefits of such controllers, gives the in-depth theory and mathematical proofs behind them, and finally discusses their applications. Each chapter includes a section for presenting the theory and mathematical definitions of one of the above mentioned algorithms. The second section of each chapter is dedicated to the examples and applications of the corresponding control algorithms in practical engineering problems. This book proposes to avoid complex mathematical equations, being generic as it includes several types of data-driven model-free controllers, such as Iterative Feedback Tuning controllers, Model-Free Controllers (intelligent PID controllers), Model-Free Adaptive Controllers, model-free sliding mode controllers, hybrid model‐free and model‐free adaptive‐Virtual Reference Feedback Tuning controllers, hybrid model-free and model-free adaptive fuzzy controllers and cooperative model-free controllers. The book includes the topic of optimal model-free controllers, as well. The optimal tuning of model-free controllers is treated in the chapters that deal with Iterative Feedback Tuning and Virtual Reference Feedback Tuning. Moreover, the extension of some model-free control algorithms to the consensus and formation-tracking problem of multi-agent dynamic systems is provided. This book can be considered as a textbook for undergraduate and postgraduate students, as well as a professional reference for industrial and academic researchers, attracting the readers from both industry and academia.
Author : Shahab Araghinejad
Publisher : Springer Science & Business Media
Page : 299 pages
File Size : 49,9 Mb
Release : 2013-11-26
Category : Science
ISBN : 9789400775060
“Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering” provides a systematic account of major concepts and methodologies for data-driven models and presents a unified framework that makes the subject more accessible to and applicable for researchers and practitioners. It integrates important theories and applications of data-driven models and uses them to deal with a wide range of problems in the field of water resources and environmental engineering such as hydrological forecasting, flood analysis, water quality monitoring, regionalizing climatic data, and general function approximation. The book presents the statistical-based models including basic statistical analysis, nonparametric and logistic regression methods, time series analysis and modeling, and support vector machines. It also deals with the analysis and modeling based on artificial intelligence techniques including static and dynamic neural networks, statistical neural networks, fuzzy inference systems, and fuzzy regression. The book also discusses hybrid models as well as multi-model data fusion to wrap up the covered models and techniques. The source files of relatively simple and advanced programs demonstrating how to use the models are presented together with practical advice on how to best apply them. The programs, which have been developed using the MATLAB® unified platform, can be found on extras.springer.com. The main audience of this book includes graduate students in water resources engineering, environmental engineering, agricultural engineering, and natural resources engineering. This book may be adapted for use as a senior undergraduate and graduate textbook by focusing on selected topics. Alternatively, it may also be used as a valuable resource book for practicing engineers, consulting engineers, scientists and others involved in water resources and environmental engineering.
Author : Edwin Lughofer
Publisher : Springer
Page : 456 pages
File Size : 45,5 Mb
Release : 2011-01-31
Category : Technology & Engineering
ISBN : 9783642180873
In today’s real-world applications, there is an increasing demand of integrating new information and knowledge on-demand into model building processes to account for changing system dynamics, new operating conditions, varying human behaviors or environmental influences. Evolving fuzzy systems (EFS) are a powerful tool to cope with this requirement, as they are able to automatically adapt parameters, expand their structure and extend their memory on-the-fly, allowing on-line/real-time modeling. This book comprises several evolving fuzzy systems approaches which have emerged during the last decade and highlights the most important incremental learning methods used. The second part is dedicated to advanced concepts for increasing performance, robustness, process-safety and reliability, for enhancing user-friendliness and enlarging the field of applicability of EFS and for improving the interpretability and understandability of the evolved models. The third part underlines the usefulness and necessity of evolving fuzzy systems in several online real-world application scenarios, provides an outline of potential future applications and raises open problems and new challenges for the next generation evolving systems, including human-inspired evolving machines. The book includes basic principles, concepts, algorithms and theoretic results underlined by illustrations. It is dedicated to researchers from the field of fuzzy systems, machine learning, data mining and system identification as well as engineers and technicians who apply data-driven modeling techniques in real-world systems.
Author : Aijun An,Jerzy Stefanowski,Sheela Ramanna,Cory Butz,Witold Pedrycz
Publisher : Springer
Page : 588 pages
File Size : 48,6 Mb
Release : 2007-08-22
Category : Computers
ISBN : 9783540725305
This book constitutes the refereed proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2007, held in Toronto, Canada in May 2007 in conjunction with the Second International Conference on Rough Sets and Knowledge Technology, RSKT 2007, both as part of the Joint Rough Set Symposium, JRS 2007.
Author : Hung T. Nguyen,Michio Sugeno
Publisher : Springer Science & Business Media
Page : 532 pages
File Size : 44,9 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9781461555056
The analysis and control of complex systems have been the main motivation for the emergence of fuzzy set theory since its inception. It is also a major research field where many applications, especially industrial ones, have made fuzzy logic famous. This unique handbook is devoted to an extensive, organized, and up-to-date presentation of fuzzy systems engineering methods. The book includes detailed material and extensive bibliographies, written by leading experts in the field, on topics such as: Use of fuzzy logic in various control systems. Fuzzy rule-based modeling and its universal approximation properties. Learning and tuning techniques for fuzzy models, using neural networks and genetic algorithms. Fuzzy control methods, including issues such as stability analysis and design techniques, as well as the relationship with traditional linear control. Fuzzy sets relation to the study of chaotic systems, and the fuzzy extension of set-valued approaches to systems modeling through the use of differential inclusions. Fuzzy Systems: Modeling and Control is part of The Handbooks of Fuzzy Sets Series. The series provides a complete picture of contemporary fuzzy set theory and its applications. This volume is a key reference for systems engineers and scientists seeking a guide to the vast amount of literature in fuzzy logic modeling and control.
Author : Renji Remesan,Jimson Mathew
Publisher : Springer
Page : 250 pages
File Size : 43,7 Mb
Release : 2014-11-03
Category : Science
ISBN : 9783319092355
This book explores a new realm in data-based modeling with applications to hydrology. Pursuing a case study approach, it presents a rigorous evaluation of state-of-the-art input selection methods on the basis of detailed and comprehensive experimentation and comparative studies that employ emerging hybrid techniques for modeling and analysis. Advanced computing offers a range of new options for hydrologic modeling with the help of mathematical and data-based approaches like wavelets, neural networks, fuzzy logic, and support vector machines. Recently machine learning/artificial intelligence techniques have come to be used for time series modeling. However, though initial studies have shown this approach to be effective, there are still concerns about their accuracy and ability to make predictions on a selected input space.
Author : Jorge Casillas,O. Cordón,Francisco Herrera Triguero,Luis Magdalena
Publisher : Springer
Page : 646 pages
File Size : 41,5 Mb
Release : 2013-06-05
Category : Technology & Engineering
ISBN : 9783540370574
Fuzzy modeling has become one of the most productive and successful results of fuzzy logic. Among others, it has been applied to knowledge discovery, automatic classification, long-term prediction, or medical and engineering analysis. The research developed in the topic during the last two decades has been mainly focused on exploiting the fuzzy model flexibility to obtain the highest accuracy. This approach usually sets aside the interpretability of the obtained models. However, we should remember the initial philosophy of fuzzy sets theory directed to serve the bridge between the human understanding and the machine processing. In this challenge, the ability of fuzzy models to express the behavior of the real system in a comprehensible manner acquires a great importance. This book collects the works of a group of experts in the field that advocate the interpretability improvements as a mechanism to obtain well balanced fuzzy models.
Author : Deepak Gupta,Aditya Khamparia,Ashish Khanna,Oscar Castillo
Publisher : Springer Nature
Page : 165 pages
File Size : 42,6 Mb
Release : 2022-01-30
Category : Technology & Engineering
ISBN : 9783030920265
This book presents a set of soft computing approaches and their application in data analytics, classification model, and control. The basics of fuzzy logic implementation for advanced hybrid fuzzy driven optimization methods has been covered in the book. The various soft computing techniques, including Fuzzy Logic, Rough Sets, Neutrosophic Sets, Type-2 Fuzzy logic, Neural Networks, Generative Adversarial Networks, and Evolutionary Computation have been discussed and they are used on variety of applications including data analytics, classification model, and control. The book is divided into two thematic parts. The first thematic section covers the various soft computing approaches for text classification and data analysis, while the second section focuses on the fuzzy driven optimization methods for the control systems. The chapters has been written and edited by active researchers, which cover hypotheses and practical considerations; provide insights into the design of hybrid algorithms for applications in data analytics, classification model, and engineering control.
Author : Scott Dick,Vladik Kreinovich,Pawan Lingras
Publisher : Springer Nature
Page : 375 pages
File Size : 53,6 Mb
Release : 2022-09-29
Category : Technology & Engineering
ISBN : 9783031160387
This book is of interest to practitioners, researchers and graduate students seeking to apply existing techniques, to learn about the state of the art, or to explore novel concepts, in the theory and application of fuzzy sets and logic. Human knowledge and judgement are essential in both designing technological systems and in evaluating their outcomes. However, humans think and communicate in imprecise concepts, not numbers. Fuzzy sets and logic are well-known, widely used approaches to bridging this gap, which have been studied for nearly 60 years. NAFIPS 2022 brought together researchers studying both the theoretical foundations of fuzzy logic and its application to real-world problems. Their work examined fuzzy solutions to problems as diverse as astronomy, chemical engineering, economics, energy engineering, health care, and transportation engineering. Many papers combined fuzzy logic with interval or probabilistic computing, neural networks, and genetic algorithms.
Author : Aminah Robinson Fayek
Publisher : Emerald Group Publishing
Page : 536 pages
File Size : 54,5 Mb
Release : 2018-10-05
Category : Technology & Engineering
ISBN : 9781787438682
This book is a guide for students, researchers, and practitioners to the latest developments in fuzzy hybrid computing in construction engineering and management. It discusses basic theory related to fuzzy logic and fuzzy hybrid computing, their application in a range of practical construction problems, and emerging and future research trends.
Author : Jun Zhao,Wei Wang,Chunyang Sheng
Publisher : Springer
Page : 443 pages
File Size : 42,9 Mb
Release : 2018-08-20
Category : Computers
ISBN : 9783319940519
This book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Related case studies, especially on energy systems in the steel industry are also addressed and analyzed. The case studies in this volume are entirely rooted in both classical data-driven prediction problems and industrial practice requirements. Detailed figures and tables demonstrate the effectiveness and generalization of the methods addressed, and the classifications of the addressed prediction problems come from practical industrial demands, rather than from academic categories. As such, readers will learn the corresponding approaches for resolving their industrial technical problems. Although the contents of this book and its case studies come from the steel industry, these techniques can be also used for other process industries. This book appeals to students, researchers, and professionals within the machine learning and data analysis and mining communities.
Author : Satchidananda Dehuri,Sung-Bae Cho
Publisher : World Scientific
Page : 324 pages
File Size : 46,9 Mb
Release : 2010-12-21
Category : Business & Economics
ISBN : 9781908978448
Knowledge Mining Using Intelligent Agents explores the concept of knowledge discovery processes and enhances decision-making capability through the use of intelligent agents like ants, termites and honey bees. In order to provide readers with an integrated set of concepts and techniques for understanding knowledge discovery and its practical utility, this book blends two distinct disciplines — data mining and knowledge discovery process, and intelligent agents-based computing (swarm intelligence and computational intelligence). For the more advanced reader, researchers, and decision/policy-makers are given an insight into emerging technologies and their possible hybridization, which can be used for activities like dredging, capturing, distributions and the utilization of knowledge in their domain of interest (i.e. business, policy-making, etc.). By studying the behavior of swarm intelligence, this book aims to integrate the computational intelligence paradigm and intelligent distributed agents architecture to optimize various engineering problems and efficiently represent knowledge from the large gamut of data. Contents: Theoretical Foundations of Knowledge Mining and Intelligent Agent (S Dehuri & S-B Cho)The Use of Evolutionary Computation in Knowledge Discovery: The Example of Intrusion Detection Systems (S X Wu & W Banzhaf)Evolution of Neural Network and Polynomial Network (B B Misra et al.)Design of Alloy Steels Using Multi-Objective Optimization (M Chen et a.)An Extended Bayesian/HAPSO Intelligent Method in Intrusion Detection System (S Dehuri & S Tripathy)Mining Knowledge from Network Intrusion Data Using Data Mining Techniques (M Panda & M R Patra)Particle Swarm Optimization for Multi-Objective Optimal Operational Planning of Energy Plants (Y Fukuyama et al.)Soft Computing for Feature Selection (A K Jagadev et al.)Optimized Polynomial Fuzzy Swarm Net for Classification (B B Misra et al.)Software Testing Using Genetic Algorithms (M Ray & D P Mohapatra) Readership: Researchers and professionals in the knowledge discovery industry. Keywords:Intelligent Agent;Knowledge Mining;Data Mining;Knowledge Discovery;Computational Intelligence;Swarm Intelligence;Evolutionary ComputationKey Features:Addresses the various issues/problems of knowledge discovery, data mining tasks and the various design challenges by the use of different intelligent agents technologiesCovers new and unique intelligent agents techniques (computational intelligence + swarm intelligence) for knowledge discovery in databases and data mining to solve the tasks of different phases of knowledge discoveryHighlights data pre-processing for knowledge mining and post-processing of knowledge that is ignored by most of the authorsConsists of a collection of well-organized chapters written by prospective authors who are actively engaged in this active area of research
