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Fuzzy Logic in Artificial Intelligence

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Abstract

After a basic introduction of fuzzy logic, we discuss its role in artificial and computational intelligence. Then we present innovative applications of fuzzy logic, focusing on fuzzy expert systems, with one typical example explored in some detail. The article concludes with suggestions how artificial intelligence and fuzzy logic can benefit from each other. I. Introduction In 1948, Alan Turing wrote a paper [1] marking the begin of a new era, the era of the intelligent machine, which raised questions that still remain unanswered today. This era was heavily influenced by the appearance of the computer, a machine that allowed humans to automate their way of thinking. However, human thinking is not exact. If you had to park your car precisely in one place, you would have extreme difficulties. To allow computers to really mimic the way humans think, the theories of fuzzy sets and fuzzy logic were created. They should be viewed as formal mathematical theories for the representation of un...
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Expertensysteme
... Alguns dos primeiros trabalhos foram feitos pela Fuji Electric em um tratamento de água e pela Hitachi em um sistema de metrô. A partir de 1990 é que algumas empresas dos EUA começam a utilizar aplicações em lógica nebulosa no contexto industrial (ABAR, 2004).Grande parte dos trabalhos que foram desenvolvidos ao longo do tempo consistiram em aplicações de sistemas de controle e de sistemas com elevada complexidade, que tentam auxiliar ou substituir o raciocínio humano(KLEMENT, 1994). Tais sistemas de controle podem ser encontrados na indústria, como uma forma de checagem de processos, em veículos de transporte, para que uma determinada função seja executada devidamente, e até em máquinas que nos auxiliam no dia a dia. ...
... Fuzzy logic is a multi-valued logic that focuses on the "degree of fact" with values of variables, which may be any actual number from 0 to 1, unlike Boolean logic, which takes either 0 or 1 as input. This mathematical tool is a way to convert partial truth as input to map in output based on activation function or transfer function (Slany, 1994). ...
... Fuzzy logic is a multi-valued logic that focuses on the "degree of fact" with values of variables, which may be any actual number from 0 to 1, unlike Boolean logic, which takes either 0 or 1 as input. This mathematical tool is a way to convert partial truth as input to map in output based on activation function or transfer function (Slany, 1994). ...
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The ever-growing energy demand for human needs has resulted in an increasing use trend of renewable energies. The use of renewable energies, particularly solar, wind etc., must be optimized with reduced effects on environmental and living creatures. For the last two decades, research has been tremendously conducted to replace all the conventional sources to decrease the dependency on exhaustible fossil fuel and their harmful environmental effects. Hence, green energy technology is considered a future alternative to meet all energy needs and related services. The present and future renewable energy (RE) goals depend on many factors. This includes the technology for optimized energy extraction from natural resources and better management and distribution systems. In light of future renewable energy (RE) goals, artificial intelligence (A.I.) is now the focus of present research and developments. This chapter deals with the critical issues and challenges of the latest Green Energy Technology with their applications using A.I.
... Fuzzy logic is a multi-valued logic that focuses on the "degree of fact" with values of variables, which may be any actual number from 0 to 1, unlike Boolean logic, which takes either 0 or 1 as input. This mathematical tool is a way to convert partial truth as input to map in output based on activation function or transfer function (Slany, 1994). ...
Chapter
Full-text available
The ever-growing energy demand for human needs has resulted in an increasing use trend of renewable energies. The use of renewable energies, particularly solar, wind etc., must be optimized with reduced effects on environmental and living creatures. For the last two decades, research has been tremendously conducted to replace all the conventional sources to decrease the dependency on exhaustible fossil fuel and their harmful environmental effects. Hence, green energy technology is considered a future alternative to meet all energy needs and related services. The present and future renewable energy (RE) goals depend on many factors. This includes the technology for optimized energy extraction from natural resources and better management and distribution systems. In light of future renewable energy (RE) goals, artificial intelligence (A.I.) is now the focus of present research and developments. This chapter deals with the critical issues and challenges of the latest Green Energy Technology with their applications using A.I.
... For fuzzy sugeno model, we are using 2 set input memberships which is drug volume and droplet size (please see figure 1). Because fuzzy was meant to solve problem using mimic human behavior [30] and then quantifying it to become machine input [31]. Therefore for fuzzy membership function input such as drug volume and droplet size was based on human experiences. ...
... For the background operation, a fuzzy logic-based system was developed that contains the rules between the input and output parameters. Fuzzy logic is one of the Artificial Intelligence (AI) tools [2]. By applying AI to model the process and develop the system is nowadays a viable solution in the baking sector [1] [3]. ...
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... ANN applies the series of mathematical equations to produce information for the biological processes such as recognition, understanding, learning (Sun et al., 2003). Fuzzy logic is a powerful problem-solving technique that generates conclusions from vague, ambiguous, incomplete, and imprecise information (Klement & Slany, 1994). GAs are adaptive search algorithms, which optimize the multivariant systems by identifying and evolving solutions until the desired combination of properties (including the formulation components or process parameters) giving optimum product performance is found. ...
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... In this way, FL ensures the opportunity to shape conditions of uncertainty. The ability to treat linguistic variables (like high and low) and making uncertain reasoning, the adaptability for problems without an exact mathematical description, the robustness with respect changing environments and rules [22], make FL fit for solving problems in many fields of engineering and applied science. ...
... Fuzzy logic, or more generally the treatment of uncertainties, is one of the classes of artificial intelligence [93], it is introduced to improve the performances of the different classical control strategies applied to variable speed drives. ...
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Book
K. Menger [Statistical metrics. Proc. Natl. Acad. Sci. USA 28, 535- 537 (1942)] proposed a probabilistic generalization of the theory of metric spaces by introducing the concept of probabilistic (statistical) metric space. This paper by Menger constituted the starting point for a field of research known as the theory of probabilistic metric spaces. This monograph presents an organized body of advanced material on this theory, incorporating much of the authors’ own research. It begins with the introductory chapter 1 devoted to historical aspects of this theory. The remaining chapters are divided into two major parts. chapters 2 through 7 develop the mathematical tools which are needed for the study of probabilistic metric spaces. This study properly begins with chapter 8 and goes through to chapter 15. Chapter 8 contains the basic definitions and simple properties. Chapters 9, 10, and 11 are devoted to special classes of probabilistic spaces: random metric spaces, distribution-generated spaces, and transformation-generated spaces. Chapters 12 and 13 deal with topologies and generalized topologies. Chapter 14 is devoted to betweenness. The final chapter is concerned with related structures such as probabilistic normed, inner-product, and information spaces. An extensive literature accompanies the text. Clearly written, this unified and self-contained monograph on probabilistic metric spaces will be particularly useful to researchers who are interested in this field. It is also suitable as a text for a graduate course on selected topics in applied probability. Probabilistic metric spaces. Available from: https://www.researchgate.net/publication/265461280_Probabilistic_metric_spaces [accessed May 11, 2015].
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“Someday, perhaps soon, we will build a machine that will be able to perform the functions of a human mind, a thinking machine” [88], the first sentence in Hillis’ book on the Connection Machine, a legendary computing machine that provided a large number of tiny processors and memory cells connected by a programmable communications network. Alan Turing probably had a very similar vision much earlier in the 20th century. What was real processor and real memory for Hillis was pencil and paper for Turing.
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The most important operations on fuzzy sets, namely intersection, union and complementation, and the addition of fuzzy numbers from the very general point of view of the theory of triangular norms which have been introduced and studied in the theory of probabilistic metric spaces and which provide a unifying concept for these operations, are discussed.