[PDF] Improved Uncertainty Modeling And Handling Using Type 2 Fuzzy Logic eBook

Author : Ondrej Linda
Publisher :
Page : 0 pages
File Size : 49,9 MB
Release : 2012
Category : Fuzzy logic
ISBN :

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Type-1 Fuzzy Logic (T1 FL) has been successfully applied in various engineering areas over the past 40 years. This fact can be attributed to the ability of T1 FL to cope with the linguistic uncertainty originating in the imprecise and vague meaning of words. However, when various kinds of data uncertainties are encountered, the performance of TI FL based systems can deteriorate. To address this issue, the concept of Type-2 (T2) FL was proposed by Lofti Zadeh in 1975 as an extension to T1 FL. The fundamental difference between T1 and T2 FL is in the model of individual Fuzzy Sets (FSs), where T2 FSs employ membership degrees that are themselves fuzzy. T2 FL has experienced a widespread of research interest in the past decade and it constitutes evolving and very active area of research. Some of the major challenges of the currently developed theory of T2 FL can be identified as follows: i) high computational complexity of T2 FL algorithms, ii) lack of established design methodology for creating robust T2 FL systems, and iii) lack of understanding of the uncertainty modeling capabilities of T2 FL systems.

Author : Asli Celikyilmaz
Publisher : Springer Science & Business Media
Page : 443 pages
File Size : 10,15 MB
Release : 2009-04-08
Category : Computers
ISBN : 3540899235

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The world we live in is pervaded with uncertainty and imprecision. Is it likely to rain this afternoon? Should I take an umbrella with me? Will I be able to find parking near the campus? Should I go by bus? Such simple questions are a c- mon occurrence in our daily lives. Less simple examples: What is the probability that the price of oil will rise sharply in the near future? Should I buy Chevron stock? What are the chances that a bailout of GM, Ford and Chrysler will not s- ceed? What will be the consequences? Note that the examples in question involve both uncertainty and imprecision. In the real world, this is the norm rather than exception. There is a deep-seated tradition in science of employing probability theory, and only probability theory, to deal with uncertainty and imprecision. The mon- oly of probability theory came to an end when fuzzy logic made its debut. H- ever, this is by no means a widely accepted view. The belief persists, especially within the probability community, that probability theory is all that is needed to deal with uncertainty. To quote a prominent Bayesian, Professor Dennis Lindley, “The only satisfactory description of uncertainty is probability.

Author : Rómulo Antão
Publisher : Springer
Page : 136 pages
File Size : 26,22 MB
Release : 2017-07-23
Category : Technology & Engineering
ISBN : 9811046336

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This book focuses on a particular domain of Type-2 Fuzzy Logic, related to process modeling and control applications. It deepens readers’understanding of Type-2 Fuzzy Logic with regard to the following three topics: using simpler methods to train a Type-2 Takagi-Sugeno Fuzzy Model; using the principles of Type-2 Fuzzy Logic to reduce the influence of modeling uncertainties on a locally linear n-step ahead predictor; and developing model-based control algorithms according to the Generalized Predictive Control principles using Type-2 Fuzzy Sets. Throughout the book, theory is always complemented with practical applications and readers are invited to take their learning process one step farther and implement their own applications using the algorithms’ source codes (provided). As such, the book offers avaluable referenceguide for allengineers and researchers in the field ofcomputer science who are interested in intelligent systems, rule-based systems and modeling uncertainty.

Author : Patricia Melin
Publisher : Springer Nature
Page : 85 pages
File Size : 21,74 MB
Release : 2021-06-03
Category : Technology & Engineering
ISBN : 3030750973

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This book presents different experimental results as evidence of the good results obtained compared with respect to conventional approaches and literature references based on fuzzy logic. Nowadays, the evolution of intelligence systems for decision making has been reached considerable levels of success, as these systems are getting more intelligent and can be of great help to experts in decision making. One of the more important realms in decision making is the area of medical diagnosis, and many kinds of intelligence systems provide the expert good assistance to perform diagnosis; some of these methods are, for example, artificial neural networks (can be very powerful to find tendencies), support vector machines, that avoid overfitting problems, and statistical approaches (e.g., Bayesian). However, the present research is focused on one of the most relevant kinds of intelligent systems, which are the fuzzy systems. The main objective of the present work is the generation of fuzzy diagnosis systems that offer competitive classifiers to be applied in diagnosis systems. To generate these systems, we have proposed a methodology for the automatic design of classifiers and is focused in the Generalized Type-2 Fuzzy Logic, because the uncertainty handling can provide us with the robustness necessary to be competitive with other kinds of methods. In addition, different alternatives to the uncertainty modeling, rules-selection, and optimization have been explored. Besides, different experimental results are presented as evidence of the good results obtained when compared with respect to conventional approaches and literature references based on Fuzzy Logic.

Author : Humberto Bustince
Publisher : Springer
Page : 674 pages
File Size : 17,91 MB
Release : 2007-10-30
Category : Computers
ISBN : 3540737235

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This carefully edited book presents an up-to-date state of current research in the use of fuzzy sets and their extensions. It pays particular attention to foundation issues and to their application to four important areas where fuzzy sets are seen to be an important tool for modeling and solving problems. The book’s 34 chapters deal with the subject with clarity and effectiveness. They include four review papers introducing some non-standard representations

Author : Dumidu Shanika Wijayasekara
Publisher :
Page : 374 pages
File Size : 47,27 MB
Release : 2016
Category : Fuzzy logic
ISBN :

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The need for automation, optimality and efficiency has made modern day control and monitoring systems extremely complex and data abundant. However, the complexity of the systems and the abundance of raw data has reduced the understandability and interpretability of data which results in a reduced state awareness of the system. Furthermore, different levels of uncertainty introduced by sensors and actuators make interpreting and accurately manipulating systems difficult. Classical mathematical methods lack the capability to capture human knowledge and increase understandability while modeling such uncertainty. Fuzzy Logic has been shown to alleviate both these problems by introducing logic based on vague terms that rely on human understandable terms. The use of linguistic terms and simple consequential rules increase the understandability of system behavior as well as data. Use of vague terms and modeling data from non-discrete prototypes enables modeling of uncertainty. However, due to recent trends, the primary research of fuzzy logic have been diverged from the basic concept of understandability. Furthermore, high computational costs to achieve robust uncertainty modeling have led to restricted use of such fuzzy systems in real-world applications. Thus, the goal of this dissertation is to present algorithms and techniques that improve understandability and uncertainty modeling using Fuzzy Logic Systems. In order to achieve this goal, this dissertation presents the following major contributions: 1) a novel methodology for generating Fuzzy Membership Functions based on understandability, 2) Linguistic Summarization of data using if-then type consequential rules, and 3) novel Shadowed Type-2 Fuzzy Logic Systems for uncertainty modeling. Finally, these presented techniques are applied to real world systems and data to exemplify their relevance and usage.

Author : Oscar Castillo
Publisher : Springer Nature
Page : 273 pages
File Size : 46,16 MB
Release : 2023-03-31
Category : Technology & Engineering
ISBN : 3031263324

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This book covers the introduction, theory, development and applications of type-2 fuzzy logic systems, which represent the current state-of-the-art in various domains such as control applications, power plants, health care, image processing, mathematical applications etc. The book is also rich in discussing different applications in order to give the researchers a flavor of how type-2 fuzzy logic is designed for different types of problems. Type-2 fuzzy logic systems are now used extensively in engineering applications for many purposes. In simple language, this book covers the practical use of type-2 fuzzy logic and its optimization through different training methods. Furthermore, this book maintains the relationship between mathematics and practical implementations in the real world. This book chapter also contains the proper comparisons with available literature work. It shows that the presented enhanced techniques have better results. This book would serve as a handy reference guide for a variety of readers, primarily targeting research scholars, undergraduate and postgraduate researchers and practicing engineers working in Type-2 fuzzy logic systems and their applications.

Author : Leticia Amador
Publisher : Springer
Page : 77 pages
File Size : 39,59 MB
Release : 2017-04-27
Category : Technology & Engineering
ISBN : 3319542958

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This book focuses on the fields of fuzzy logic, bio-inspired algorithm; especially bee colony optimization algorithm and also considering the fuzzy control area. The main idea is that this areas together can to solve various control problems and to find better results. In this book we test the proposed method using two benchmark problems; the problem for filling a water tank and the problem for controlling the trajectory in an autonomous mobile robot. When Interval Type-2 Fuzzy Logic System is implemented to model the behavior of systems, the results show a better stabilization, because the analysis of uncertainty is better. For this reason we consider in this book the proposed method using fuzzy systems, fuzzy controllers, and bee colony optimization algorithm improve the behavior of the complex control problems.