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Author: Dr. Parveen Chauhan
Publisher: Xoffencerpublication
ISBN: 811953428X
Category : Business & Economics
Languages : en
Pages : 303
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Book Description
The concept of fuzzy logic refers to a specific subset of many-valued logic. In this line of reasoning, the truth value of a variable can be any real integer, including any fraction that is between 0 and 1. This applies to all fractions as well. It achieves this by regulating the concept of partial truth, in which the truth value may switch between being entirely true and entirely false at any given moment. This objective may be accomplished by making use of the tool for managing concepts. In contrast, the truth values of variables in Boolean logic can never be anything other than the integer values 0 or 1, as there are only two alternatives that even have a remote chance of occurring. This is because there are only two options that are even remotely imaginable. It is common practice to consider the fuzzy set theory, which was created in 1965 by the Iranian-Azerbaijani mathematician Lotfi Zadeh, to be the basis for fuzzy logic. However, since the 1920s, scholars have been investigating fuzzy logic, which was also known as infinite-valued logic at the time. Most notably, Lukasiewicz and Tarski were the researchers that began this line of inquiry. This particular investigation didn't wrap up until the 1960s, but it began in the 1920s. The idea of fuzzy logic is based on the fact that decision-makers frequently rely on hazy and non-numerical information. In other words, this is the origin of fuzzy logic. The mathematical methods of fuzzy modeling and fuzzy set creation, both of which are used to describe ambiguous and imprecise information, are where the name "fuzzy" first appeared. These models are capable of recognizing, representing, manipulating, understanding, and using facts and information that are fundamentally hazy and ambiguous in nature. Fuzzy logic has been effectively applied in a variety of applications, from control theory to artificial intelligence. Conventional patterns of thinking can only ever lead to conclusions that are either correct or incorrect. However, there are other statements that may elicit a range of responses, such as the answers you could get if you asked a group of individuals to name a color. One that invites people to name a meal is another 1 | P a ge illustration of this kind of proposal. In situations like this, it is the application of reasoning based on incomplete or inaccurate information that leads to the finding of the truth. This argument entails plotting the sampled responses on a spectrum. Although degrees of truth and probabilities both range from 0 to 1, fuzzy logic employs degrees of truth as a mathematical model of ambiguity whereas probability is a mathematical model of ignorance, despite the fact that they may initially appear to be the same. Although they could at first glance appear to be the same because both probability and degrees of truth range from 0 to 1, this is only because they do.
Author: Dr. Parveen Chauhan
Publisher: Xoffencerpublication
ISBN: 811953428X
Category : Business & Economics
Languages : en
Pages : 303
Get Book Here
Book Description
The concept of fuzzy logic refers to a specific subset of many-valued logic. In this line of reasoning, the truth value of a variable can be any real integer, including any fraction that is between 0 and 1. This applies to all fractions as well. It achieves this by regulating the concept of partial truth, in which the truth value may switch between being entirely true and entirely false at any given moment. This objective may be accomplished by making use of the tool for managing concepts. In contrast, the truth values of variables in Boolean logic can never be anything other than the integer values 0 or 1, as there are only two alternatives that even have a remote chance of occurring. This is because there are only two options that are even remotely imaginable. It is common practice to consider the fuzzy set theory, which was created in 1965 by the Iranian-Azerbaijani mathematician Lotfi Zadeh, to be the basis for fuzzy logic. However, since the 1920s, scholars have been investigating fuzzy logic, which was also known as infinite-valued logic at the time. Most notably, Lukasiewicz and Tarski were the researchers that began this line of inquiry. This particular investigation didn't wrap up until the 1960s, but it began in the 1920s. The idea of fuzzy logic is based on the fact that decision-makers frequently rely on hazy and non-numerical information. In other words, this is the origin of fuzzy logic. The mathematical methods of fuzzy modeling and fuzzy set creation, both of which are used to describe ambiguous and imprecise information, are where the name "fuzzy" first appeared. These models are capable of recognizing, representing, manipulating, understanding, and using facts and information that are fundamentally hazy and ambiguous in nature. Fuzzy logic has been effectively applied in a variety of applications, from control theory to artificial intelligence. Conventional patterns of thinking can only ever lead to conclusions that are either correct or incorrect. However, there are other statements that may elicit a range of responses, such as the answers you could get if you asked a group of individuals to name a color. One that invites people to name a meal is another 1 | P a ge illustration of this kind of proposal. In situations like this, it is the application of reasoning based on incomplete or inaccurate information that leads to the finding of the truth. This argument entails plotting the sampled responses on a spectrum. Although degrees of truth and probabilities both range from 0 to 1, fuzzy logic employs degrees of truth as a mathematical model of ambiguity whereas probability is a mathematical model of ignorance, despite the fact that they may initially appear to be the same. Although they could at first glance appear to be the same because both probability and degrees of truth range from 0 to 1, this is only because they do.
Author: Santosh Kumar Das
Publisher: CRC Press
ISBN: 1000718530
Category : Mathematics
Languages : en
Pages : 210
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Book Description
This book helps to enhance the application of fuzzy logic optimization in the areas of science and engineering. It includes implementation and models and paradigms, such as path planning and routing design for different wireless networks, organization behavior strategies models, and so forth. It also: Explains inventory control management, uncertainties management, loss minimization, game optimization, data analysis and prediction, different decision-making system and management, and so forth Describes applicability of fuzzy optimization techniques in areas of science and management Resolves several issues based on uncertainty using member function Helps map different problems based on mathematical models Includes issues and problems based on linear and nonlinear optimizations Focuses on management science such as manpower management and inventory planning This book is aimed at researchers and graduate students in signal processing, power systems, systems and industrial engineering, and computer networks.
Author: Wang, John
Publisher: IGI Global
ISBN: 1466652039
Category : Business & Economics
Languages : en
Pages : 2862
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Book Description
As the age of Big Data emerges, it becomes necessary to take the five dimensions of Big Data- volume, variety, velocity, volatility, and veracity- and focus these dimensions towards one critical emphasis - value. The Encyclopedia of Business Analytics and Optimization confronts the challenges of information retrieval in the age of Big Data by exploring recent advances in the areas of knowledge management, data visualization, interdisciplinary communication, and others. Through its critical approach and practical application, this book will be a must-have reference for any professional, leader, analyst, or manager interested in making the most of the knowledge resources at their disposal.
Author: Ali Ebrahimnejad
Publisher: Springer
ISBN: 3319739034
Category : Technology & Engineering
Languages : en
Pages : 371
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Book Description
The book offers a comprehensive, practice-oriented introduction to the field of fuzzy mathematical programming (FMP) as key topic of modern analytics. FMP plays a fundamental role in dealing with a varied range of problems, such as those concerning smart cities, sustainability, and renewable energies. This book includes an introduction to the basic concepts, together with extensive information on the computational-intelligence-based optimization models and techniques that have been used to date. Special emphasis is given to fuzzy transportation problems. The book is a valuable resource for researchers, data scientists and practitioners dealing with computational-intelligence-based optimization models for analytics.
Author: Mario Fedrizzi
Publisher: Springer Science & Business Media
ISBN: 3642457002
Category : Business & Economics
Languages : en
Pages : 227
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Book Description
The title of this book seems to indicate that the volume is dedicated to a very specialized and narrow area, i. e. , to the relationship between a very special type of optimization and mathematical programming. The contrary is however true. Optimization is certainly a very old and classical area which is of high concern to many disciplines. Engineering as well as management, politics as well as medicine, artificial intelligence as well as operations research, and many other fields are in one way or another concerned with optimization of designs, decisions, structures, procedures, or information processes. It is therefore not surprising that optimization has not grown in a homogeneous way in one discipline either. Traditionally, there was a distinct difference between optimization in engineering, optimization in management, and optimization as it was treated in mathematical sciences. However, for the last decades all these fields have to an increasing degree interacted and contributed to the area of optimization or decision making. In some respects, new disciplines such as artificial intelligence, descriptive decision theory, or modern operations research have facilitated, or even made possible the interaction between the different classical disciplines because they provided bridges and links between areas which had been developing and applied quite independently before. The development of optimiiation over the last decades can best be appreciated when looking at the traditional model of optimization. For a well-structured, Le.
Author: Sachi Nandan Mohanty
Publisher: John Wiley & Sons
ISBN: 1119864925
Category : Technology & Engineering
Languages : en
Pages : 373
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Book Description
FUZZY COMPUTING IN DATA SCIENCE This book comprehensively explains how to use various fuzzy-based models to solve real-time industrial challenges. The book provides information about fundamental aspects of the field and explores the myriad applications of fuzzy logic techniques and methods. It presents basic conceptual considerations and case studies of applications of fuzzy computation. It covers the fundamental concepts and techniques for system modeling, information processing, intelligent system design, decision analysis, statistical analysis, pattern recognition, automated learning, system control, and identification. The book also discusses the combination of fuzzy computation techniques with other computational intelligence approaches such as neural and evolutionary computation. Audience Researchers and students in computer science, artificial intelligence, machine learning, big data analytics, and information and communication technology.
Author: Rajendra Akerkar
Publisher: Springer
ISBN: 3319292064
Category : Computers
Languages : en
Pages : 282
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Book Description
This textbook provides readers with the tools, techniques and cases required to excel with modern artificial intelligence methods. These embrace the family of neural networks, fuzzy systems and evolutionary computing in addition to other fields within machine learning, and will help in identifying, visualizing, classifying and analyzing data to support business decisions./p> The authors, discuss advantages and drawbacks of different approaches, and present a sound foundation for the reader to design and implement data analytic solutions for real‐world applications in an intelligent manner. Intelligent Techniques for Data Science also provides real-world cases of extracting value from data in various domains such as retail, health, aviation, telecommunication and tourism.
Author: Bansal, Sanjeev
Publisher: IGI Global
ISBN:
Category : Business & Economics
Languages : en
Pages : 377
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Book Description
Today, the convergence of cutting-edge algorithms and actionable insights in business is paramount for success. Scholars and practitioners grapple with the dilemma of optimizing data to drive efficiency, innovation, and competitiveness. The formidable challenge of effectively harnessing the immense power of intelligent optimization techniques and business analytics only increases as the volume of data grows exponentially, and the complexities of navigating the intricate landscape of business analytics becomes more daunting. This pressing issue underscores the critical need for a comprehensive solution, and Intelligent Optimization Techniques for Business Analytics is poised to provide much-needed answers. This groundbreaking book offers an all-encompassing solution to the challenges that academic scholars encounter in the pursuit of mastering the interplay between learning algorithms and intelligent optimization techniques for business analytics. Through a wealth of diverse perspectives and expert case studies, it illuminates the path to effectively implementing these advanced systems in real-world business scenarios. It caters not only to the scholarly community but also to industry professionals and policymakers, equipping them with the necessary tools and insights to excel in the realm of data-driven decision-making.
Author: Weldon A. Lodwick
Publisher: Springer Science & Business Media
ISBN: 3642139345
Category : Computers
Languages : en
Pages : 535
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Book Description
This potent area of technology allows us to formulate and solve a multitude of problems. Written by leading experts, this overview covers a number of aspects of fuzzy optimization, some related general issues, and various applications of this powerful tool.
Author: Cengiz Kahraman
Publisher: Springer
ISBN: 3030237567
Category : Technology & Engineering
Languages : en
Pages : 1392
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Book Description
This book includes the proceedings of the Intelligent and Fuzzy Techniques INFUS 2019 Conference, held in Istanbul, Turkey, on July 23–25, 2019. Big data analytics refers to the strategy of analyzing large volumes of data, or big data, gathered from a wide variety of sources, including social networks, videos, digital images, sensors, and sales transaction records. Big data analytics allows data scientists and various other users to evaluate large volumes of transaction data and other data sources that traditional business systems would be unable to tackle. Data-driven and knowledge-driven approaches and techniques have been widely used in intelligent decision-making, and they are increasingly attracting attention due to their importance and effectiveness in addressing uncertainty and incompleteness. INFUS 2019 focused on intelligent and fuzzy systems with applications in big data analytics and decision-making, providing an international forum that brought together those actively involved in areas of interest to data science and knowledge engineering. These proceeding feature about 150 peer-reviewed papers from countries such as China, Iran, Turkey, Malaysia, India, USA, Spain, France, Poland, Mexico, Bulgaria, Algeria, Pakistan, Australia, Lebanon, and Czech Republic.
