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FUZZY OPTIMIZATION FOR BUSINESS ANALYTICS AND DATA SCIENCE
Dr. Parveen ChauhanRashmi Rani PatroRojalini PatroDr Bibin K Jose
出版Xoffencerpublication, 2023-08-21
主題Business & Economics / E-Commerce / Search Engine Optimization
ISBN811953428X9788119534289
URLhttp://books.google.com.hk/books?id=lOwhEQAAQBAJ&hl=&source=gbs_api
EBookSAMPLE
註釋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.