Incorporate nuance, because of fuzzy logic

Author(s):  
Susan D'Agostino

“Incorporate nuance, because of fuzzy logic” offers a basic introduction to fuzzy logic, a multi-valued logical system that accommodates a range of absolute and partial truths. Fuzzy logic differs from traditional logic in which statements are considered either absolutely true or absolutely false. Fuzzy logic offers a method for programming computers with intelligent instructions that emulate human thought and decision making. The discussion is supplemented with numerous hand-drawn sketches and explanations of real-life applications of fuzzy logic in electric trains, washing machines, digital cameras, rice cookers, facial recognition software, drones, and medical devices. Mathematics students and enthusiasts are encouraged to fuzzify their mathematical and life pursuits involving uncertain circumstances in which an absolute “yes” or an absolute “no” may not be the best decision. At the chapter’s end, readers may check their understanding by working on a problem. A solution is provided.

2017 ◽  
Vol 4 (2) ◽  
Author(s):  
Edwin Mejía Peñafiel ◽  
Alberto Leopoldo Arellano Aucancela ◽  
Geovanny Vallejo

En los últimos años la inteligencia artificial ha ido aumentando su nivel en cuanto a investigación se refiere, los sistemas difusos se han venido consolidando como una herramienta útil para modelar sistemas complejos y no lineales. Las técnicas de la inteligencia artificial se han convertido en una herramienta fundamental para abordar problemas complejos incluyendo el área de control automático. A diferencia de la lógica tradicional que solo utiliza dos valores de verdadero o falso, la lógica difusa permite definir valores intermedios en un intento por aplicar un modo de pensamiento similar al del ser humano. En esta situación los sistemas expertos tienen mucho que ver con lo que significa inferir conocimiento, utilizando las famosas reglas de inferencia o también conocidas como reglas de producción, dentro de la lógica difusa se utilizará el método de inferencia de Mandani que hace uso de las reglas Si X Entonces Y, si premisa entonces conclusión. En este artículo se ha desarrollado un algoritmo difuso para controlar la velocidad de un auto y evitar que el mismo choque cuando el conductor sufre cualquier alteración de su cuerpo, el prototipo recoge información de su entorno para la toma de decisiones, se presenta un modelo como prototipo a seguir en este caso para la construcción, se hace un análisis de los diferentes dispositivos y se presentan los conceptos.  Abstract In recent years artificial intelligence has been increasing its level in terms of research, diffuse systems have been consolidated as a useful tool for modeling complex and non-linear systems. Artificial intelligence techniques have become a fundamental tool for addressing complex problems including the automatic control area. Unlike traditional logic that uses only two values ​​of true or false, fuzzy logic allows defining intermediate values ​​in an attempt to apply a mode of thinking similar to that of the human being. In this situation, the expert systems have much to do with what it means to infer knowledge, using the famous rules of inference or also known as rules of production, within the fuzzy logic will be used the method of inference of Mandani that makes use of the rules If X Then Y, if premise then conclusion. In this article we have developed a diffuse algorithm to control the speed of a car and prevent the same shock when the driver suffers any alteration of his body, the prototype collects information from its environment for decision making, a model is presented as Prototype to follow in this case for the construction, is made an analysis of the different devices and the concepts are presented.


Informatica ◽  
2018 ◽  
Vol 29 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Javier Albadán ◽  
Paulo Gaona ◽  
Carlos Montenegro ◽  
Rubén González-Crespo ◽  
Enrique Herrera-Viedma

Author(s):  
Sri Handayani Sianipar ◽  
Fince Tinus Waruwu ◽  
Lince Tomoria Sianturi

Ulos batak toba is one of indonesia traditional fabric, precisely the traditional cloth of the batak toba. From time to time the ulos fabric was growing in terms of  type and motif. One of the companies that produces ulos batak is cv. Ala dos roha. The authors conducted this study aimed at predicting the amount of production of ulos batak to produced later. The author uses the previous request, inventory and production data using fuzzy logic tsukamoto. The final result of the calculation with this method will be more effective and efficient so as to speed up the decision making time to predict the amount of production to be produced next.Keywords: prediction, amount of  production, method of tsukamoto


2016 ◽  
Vol 7 (1) ◽  
pp. 12-18
Author(s):  
Joko Haryanto ◽  
Seng Hansun

This paper describes the development of decision support system application to assist students who want to enter college so that no one choose the majors incorrectly. This application uses fuzzy logic method because fuzzy logic is very flexible in data which are vague and can be represented as a linguistic variable. The purpose of this application is to assist students to choose available majors at University Multimedia Nusantara which are appropriate with his/her capabilities. This application accepts five kinds of input values i.e. Mathematics, Indonesian, English, Physics, and TIK. Received input will be processed by the calculation of the system for decision-making and the application will generate output that shows how great a match for each majors. With this application, prospective students can find out where the majors that match his/her capabilities. This application has ninety nine percentage of match result accuracy. Index Terms—fuzzy logic, decision support system, UMN, selection of major


Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 774
Author(s):  
Adis Puška ◽  
Miroslav Nedeljković ◽  
Sarfaraz Hashemkhani Zolfani ◽  
Dragan Pamučar

The selection of sustainable suppliers (SSS) is the first step in applying a sustainable supply chain and sustainable production. Therefore, it is necessary to select the supplier that best meets the set sustainability criteria. However, the selection of suppliers cannot be done by applying symmetric information, because the company does not have complete information, so asymmetric information should be used when selecting suppliers. Since the SSS applies three main sustainability criteria, environmental, social, and economic criteria, this decision-making problem is solved by applying multi-criteria decision-making (MCDM). In order to solve the SSS for the needs of agricultural production, interval fuzzy logic was applied in this research, and six suppliers with whom agricultural pharmacies in Semberija work were taken into consideration. The application of interval fuzzy logic was performed using the methods PIPRECIA (Pivot pairwise relative criteria importance assessment) and MABAC (Multi-Attributive Border Approximation Area Comparison). Using the PIPRECIA method, the weights of criteria and sub-criteria were determined. Results of this method showed that the most significant are economic criteria, followed by the social criteria. The ecological criteria are the least important. The supplier ranking was performed using the MABAC method. The results showed that supplier A4 best meets the sustainability criteria, while supplier A6 is the worst. These results were confirmed using other MCDM methods, followed by the sensitivity analysis. According to the attained results, agricultural producers from Semberija should buy the most products from suppliers A4, in order to better apply sustainability in production. This paper showed how to decision make when there is asymmetric information about suppliers.


Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2617
Author(s):  
Catalin Dumitrescu ◽  
Petrica Ciotirnae ◽  
Constantin Vizitiu

When considering the concept of distributed intelligent control, three types of components can be defined: (i) fuzzy sensors which provide a representation of measurements as fuzzy subsets, (ii) fuzzy actuators which can operate in the real world based on the fuzzy subsets they receive, and, (iii) the fuzzy components of the inference. As a result, these elements generate new fuzzy subsets from the fuzzy elements that were previously used. The purpose of this article is to define the elements of an interoperable technology Fuzzy Applied Cell Control-soft computing language for the development of fuzzy components with distributed intelligence implemented on the DSP target. The cells in the network are configured using the operations of symbolic fusion, symbolic inference and fuzzy–real symbolic transformation, which are based on the concepts of fuzzy meaning and fuzzy description. The two applications presented in the article, Agent-based modeling and fuzzy logic for simulating pedestrian crowds in panic decision-making situations and Fuzzy controller for mobile robot, are both timely. The increasing occurrence of panic moments during mass events prompted the investigation of the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. Based on the research presented in the article, we propose a Fuzzy controller-based system for determining pedestrian flows and calculating the shortest evacuation distance in panic situations. Fuzzy logic, one of the representation techniques in artificial intelligence, is a well-known method in soft computing that allows the treatment of strong constraints caused by the inaccuracy of the data obtained from the robot’s sensors. Based on this motivation, the second application proposed in the article creates an intelligent control technique based on Fuzzy Logic Control (FLC), a feature of intelligent control systems that can be used as an alternative to traditional control techniques for mobile robots. This method allows you to simulate the experience of a human expert. The benefits of using a network of fuzzy components are not limited to those provided distributed systems. Fuzzy cells are simple to configure while also providing high-level functions such as mergers and decision-making processes.


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