Content uploaded by Erich Peter Klement

Author content

All content in this area was uploaded by Erich Peter Klement on Sep 08, 2014

Content may be subject to copyright.

Labor

Expertensysteme

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...

Content uploaded by Erich Peter Klement

Author content

All content in this area was uploaded by Erich Peter Klement on Sep 08, 2014

Content may be subject to copyright.

Labor

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). ...

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). ...

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]. ...

This paper introduces a new method for production development with the usage of process observation, fuzzy logic, and sensoring. In collaboration with experts in the field of food science, design, a program was developed that can simulate the behaviour of a semi-finished product during preparation. A factory producing bakery products applied sensors in order to gain real-time data that can be integrated into the program. Because of all the uncertainty of a preparation process, the optimal baking temperature and time are different in each package of the product. With the previously mentioned program, it is possible to provide the optimal values for each package.

... 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. ...

This chapter focuses on the optimization of various process and formulation parameters for the development of parenteral dosage form using DOE. The researchers encounter complex technical challenges during formulation development, which makes it imperative to use an effective methodology for formulation development. DOE and statistical analysis are a promising tool for the optimization of different formulation and process variables. The considerable advantage of applying DOE to develop formulations for pharmaceutical products is that it permits all crucial variables to be evaluated systematically and accurately. Once the important variables have been identified, the optimal formulations can be finalized by using accurate DOE to optimize the levels of all critical variables. DoE is the first choice for rational pharmaceutical development for researchers.

... 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. ...

Conventional direct torque control (DTC) is one of the excellent control strategies available to control the torque of the induction machine (IM). However, the low switching frequency of the DTC causes high ripples in the flux and torque that leads to an acoustic noise which degrades the control performances, especially at low speeds. Many direct torque control techniques were appeared to remedy these problems by focusing specifically on the torque and flux. In this paper, a state of the art review of various modern techniques for improving the performance of DTC control is presented. The objective is to make a critical analysis of these methods in terms of ripples reduction, tracking speed, switching loss, algorithm complexity and parameter sensitivity. Further, it is envisaged that the information presented in this review paper will be a valuable gathering of information for academic and industrial researchers.

Metallic glass (MG) is a promising coating material developed to enhance the surface hardness of metallic substrates, with laser cladding having become popular to develop such coatings. MGs properties are affected by the laser cladding variables (laser power, scanning speed, spot size). Meanwhile, the substrate surface roughness significantly affects the geometry and hardness of the laser-cladded MG. In this research, Fe-based MG was laser-cladded on substrates with different surface roughness. For this purpose, the surfaces of the substrate were prepared for cladding using two methods: sandpaper polishing (SP) and sandblasting (SB), with two levels of grit size used for each method (SP150, SP240, SB40, SB100). The experiment showed that substrate surface roughness affected the geometry and hardness of laser-cladded Fe-based MG. To predict and optimize the geometry and hardness of laser-cladded Fe-based MG single tracks at different substrate surface roughness, a fuzzy logic control system (FLCS) was developed. The FLCS results indicate that it is an efficient tool to select the proper preparation technique of the substrate surface for higher clad hardness and maximum geometry to minimize the number of cladding tracks for full surface cladding.

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].

Scitation is the online home of leading journals and conference proceedings from AIP Publishing and AIP Member Societies

“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.

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.