Fuzzy logic and fuzzy systems

2022 ◽  
pp. 153-176
Author(s):  
Carlos A. Reyes-García ◽  
Alejandro A. Torres-García
Keyword(s):  
1996 ◽  
Vol 12 (02) ◽  
pp. 85-98
Author(s):  
Jun Li ◽  
Michael G. Parsons

Fuzzy logic is a technique that attempts to systematically and mathematically emulate human reasoning. This paper investigates the feasibility of applying fuzzy logic to transportation and shipbuilding market modeling, analysis and forecasting. Fuzzy systems called fuzzy decision modelers (FDMs) are developed based on fuzzy logic techniques to model the crude oil tanker freight rate market, the tanker new order market and the tanker scrapping market. Our results show that the FDMs are able to model and forecast these economic systems very well. In addition, the FDMs also provide valuable insights into market mechanisms and market participants' decision-making patterns. The FDMs are mathematical model-free, nonlinear systems capable of capturing complicated relationships among economic variables. The FDMs are easy to develop and easy to interpret. These advantages of fuzzy systems suggest that fuzzy logic techniques are a promising alternative in shipping and shipbuilding market modeling, analysis and forecasting.


2017 ◽  
Vol 1 (1) ◽  
pp. 22
Author(s):  
Khairul Saleh

Abstract - In the world of education to achieve the level of success, of course, they have a benchmark for the success of students, one of them is the Grade Point Average (GPA). The purpose of this study is to determine the final GPA so that later it can be used as a reference to predict the success rate of students. The issue of decision-making systems using Fuzzy systems is very suitable for definite reasoning or estimation, especially for systems with strict mathematical models that are difficult to get a definite decision. Fuzzy logic can be used to describe a system of chaotic dynamics, and fuzzy logic can be useful for complex dynamic systems where solutions to common mathematical models cannot work well. The Mamdani method computes efficiently and works well with optimization and adaptive techniques, which makes it very good in control problems, especially for dynamic non-linear systems. Keywords - Cumulative Achievement Index (GPA), fuzzy system, decision making system, mamdani information


Author(s):  
M. Mohammadian

With increased application of fuzzy logic in complex control systems, there is a need for a structured methodological approach in the development of fuzzy logic systems. Current fuzzy logic systems are developed based on individualistic bases and cannot face the challenge of interacting with other (fuzzy) systems in a dynamic environment. In this chapter a method for development of fuzzy systems that can interact with other (fuzzy) systems is proposed. Specifically a method for designing hierarchical self-learning fuzzy logic control systems based on the integration of genetic algorithms and fuzzy logic to provide an integrated knowledge base for intelligent control of mobile robots for collision-avoidance in a common workspace. The robots are considered as point masses moving in a common work space. Genetic algorithms are employed as an adaptive method for learning the fuzzy rules of the control systems as well as learning, the mapping and interaction between fuzzy knowledge bases of different fuzzy logic systems.


2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Leticia Cervantes ◽  
Oscar Castillo ◽  
Denisse Hidalgo ◽  
Ricardo Martinez-Soto

We propose to use an approach based on fuzzy logic for the adaptation of gap generation and mutation probability in a genetic algorithm. The performance of this method is presented with the benchmark problem of flight control and results show how it can decrease the error during the flight of an airplane using fuzzy logic for some parameters of the genetic algorithm. In this case of study, we use fuzzy systems for adapting two parameters of the genetic algorithm to improve the design of a type 2 fuzzy controller and enhance its performance to achieve flight control. Finally, a statistical test is presented to prove the performance enhancement in the application using fuzzy adaptation in the genetic algorithm. It is important to mention that not only is this idea for control problems but also it can be used in pattern recognition and many different problems.


Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2818
Author(s):  
Pedro J. Correa-Caicedo ◽  
Horacio Rostro-González ◽  
Martin A. Rodriguez-Licea ◽  
Óscar Octavio Gutiérrez-Frías ◽  
Carlos Alonso Herrera-Ramírez ◽  
...  

GPS sensors are widely used to know a vehicle’s location and to track its route. Although GPS sensor technology is advancing, they present systematic failures depending on the environmental conditions to which they are subjected. To tackle this problem, we propose an intelligent system based on fuzzy logic, which takes the information from the sensors and correct the vehicle’s absolute position according to its latitude and longitude. This correction is performed by two fuzzy systems, one to correct the latitude and the other to correct the longitude, which are trained using the MATLAB ANFIS tool. The positioning correction system is trained and tested with two different datasets. One of them collected with a Pmod GPS sensor and the other a public dataset, which was taken from routes in Brazil. To compare our proposal, an unscented Kalman filter (UKF) was implemented. The main finding is that the proposed fuzzy systems achieve a performance of 69.2% higher than the UKF. Furthermore, fuzzy systems are suitable to implement in an embedded system such as the Raspberry Pi 4. Another finding is that the logical operations facilitate the creation of non-linear functions because of the ‘if else’ structure. Finally, the existence justification of each fuzzy system section is easy to understand.


2019 ◽  
Vol 14 (2) ◽  
pp. 174-186
Author(s):  
Tajul Rosli Razak ◽  
Iman Hazwam Abd Halim ◽  
Muhammad Nabil Fikri Jamaludin ◽  
Mohammad Hafiz Ismail ◽  
Shukor Sanim Mohd Fauzi

Recommendation system, also known as a recommender system, is a tool to help the user in providing asuggestion of a specific dilemma. Recently, the interest in developing a recommendation system in manyfields has increased. Fuzzy Logic system (FLSs) is one of the approaches that can be used to model therecommendation systems as it can deal with uncertainty and imprecise information. However, one of thefundamental issues in FLS is the problem of the curse of dimensionality. That is, the number of rules inFLSs is increasing exponentially with the number of input variables. One effective way to overcome thisproblem is by using Hierarchical Fuzzy System (HFSs). This paper aims to explore the use of HFSs forRecommendation system. Specifically, we are interested in exploring and comparing the HFS and FLS forthe Career path recommendation system (CPRS) based on four key criteria, namely topology, the numberof rules, the rules structures and interpretability. The findings suggested that the HFS has advantagesover FLS towards improving the interpretability models, in the context of a recommendation systemexample. This study contributes to providing an insight into the development of interpretable HFSs in theRecommendation systems. Keywords: Fuzzy Logic Systems, Hierarchical Fuzzy Systems, Recommendation Systems


Author(s):  
Larbi Esmahi ◽  
Kristian Williamson ◽  
Elarbi Badidi

Fuzzy logic became the core of a different approach to computing. Whereas traditional approaches to computing were precise, or hard edged, fuzzy logic allowed for the possibility of a less precise or softer approach (Klir et al., 1995, pp. 212-242). An approach where precision is not paramount is not only closer to the way humans thought, but may be in fact easier to create as well (Jin, 2000). Thus was born the field of soft computing (Zadeh, 1994). Other techniques were added to this field, such as Artificial Neural Networks (ANN), and genetic algorithms, both modeled on biological systems. Soon it was realized that these tools could be combined, and by mixing them together, they could cover their respective weaknesses while at the same time generate something that is greater than its parts, or in short, creating synergy. Adaptive Neuro-fuzzy is perhaps the most prominent of these admixtures of soft computing technologies (Mitra et al., 2000). The technique was first created when artificial neural networks were modified to work with fuzzy logic, hence the Neuro-fuzzy name (Jang et al., 1997, pp. 1-7). This combination provides fuzzy systems with adaptability and the ability to learn. It was later shown that adaptive fuzzy systems could be created with other soft computing techniques, such as genetic algorithms (Yen et al., 1998, pp. 469-490), Rough sets (Pal et al., 2003; Jensen et al., 2004, Ang et al., 2005) and Bayesian networks (Muller et al., 1995), but the Neuro-fuzzy name was widely used, so it stayed. In this chapter we are using the most widely used terminology in the field. Neuro-fuzzy is a blanket description of a wide variety of tools and techniques used to combine any aspect of fuzzy logic with any aspect of artificial neural networks. For the most part, these combinations are just extensions of one technology or the other. For example, neural networks usually take binary inputs, but use weights that vary in value from 0 to 1. Adding fuzzy sets to ANN to convert a range of input values into values that can be used as weights is considered a Neuro-fuzzy solution. This chapter will pay particular interest to the sub-field where the fuzzy logic rules are modified by the adaptive aspect of the system. The next part of this chapter will be organized as follows: in section 1 we examine models and techniques used to combine fuzzy logic and neural networks together to create Neuro-fuzzy systems. Section 2 provides an overview of the main steps involved in the development of adaptive Neuro-fuzzy systems. Section 3 concludes this chapter with some recommendations and future developments.


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