Biological Models via Interval Type-2 Fuzzy Sets
Biological Models via Interval Type-2 Fuzzy Sets

Author: Rosana Sueli da Motta Jafelice

Publisher: Springer Nature

Published: 2021-01-12

Total Pages: 152

ISBN-13: 3030645304

DOWNLOAD EBOOK

This book offers a gentle introduction to type-2 fuzzy sets and, in particular, interval type-2 fuzzy sets and their application in biological modeling. Interval type-2 fuzzy modeling is a comparatively recent direction of research in fuzzy modeling. As the modeling of biological problems is inherently uncertain, the use of fuzzy sets in this field is a natural choice. The coverage begins with a succinct review of type-1 fuzzy basic theory, before providing a comprehensive and didactic explanation of type-2 fuzzy set components. In turn, Fuzzy Rule-Based Systems, or FRBS, are shown for both types, interval type-2 and type-1 fuzzy sets. Applications include the pharmacological models, prediction of prostate cancer stages, a model for HIV population transfer (asymptomatic to symptomatic), an epidemiological disease caused by HIV, some models in population growth, included the Malthus Model, and an epidemic model refers to COVID-19. The book is ideally suited to graduate students in mathematics and related fields, professionals, researchers, or the public interested in interval type-2 fuzzy modeling. Largely self-contained, it can also be used as a supplementary text in specialized graduate courses.

Bio-Inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition
Bio-Inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition

Author: Patricia Melin

Publisher: Springer

Published: 2009-11-19

Total Pages: 258

ISBN-13: 3642045162

DOWNLOAD EBOOK

Bio-Inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition comprises papers on diverse aspects of bio-inspired models, soft computing and hybrid intelligent systems. The articles are divided into four main parts. The first one consists of papers that propose new fuzzy and bio-inspired models to solve general problems. The second part deals with the main theme of modular neural networks in pattern recognition, which are basically papers using bio-inspired techniques. The third part contains papers that apply hybrid intelligent systems to the problem of time series analysis and prediction, while the fourth one shows papers dealing with bio-inspired models in optimization and robotics applications. An edited book in which both theoretical and application aspects are covered.

Analysis and Synthesis for Interval Type-2 Fuzzy-Model-Based Systems
Analysis and Synthesis for Interval Type-2 Fuzzy-Model-Based Systems

Author: Hongyi Li

Publisher: Springer

Published: 2016-02-29

Total Pages: 263

ISBN-13: 9811005931

DOWNLOAD EBOOK

This book develops a set of reference methods capable of modeling uncertainties existing in membership functions, and analyzing and synthesizing the interval type-2 fuzzy systems with desired performances. It also provides numerous simulation results for various examples, which fill certain gaps in this area of research and may serve as benchmark solutions for the readers. Interval type-2 T-S fuzzy models provide a convenient and flexible method for analysis and synthesis of complex nonlinear systems with uncertainties.

Ensembles of Type 2 Fuzzy Neural Models and Their Optimization with Bio-Inspired Algorithms for Time Series Prediction
Ensembles of Type 2 Fuzzy Neural Models and Their Optimization with Bio-Inspired Algorithms for Time Series Prediction

Author: Jesus Soto

Publisher: Springer

Published: 2017-11-19

Total Pages: 103

ISBN-13: 3319712640

DOWNLOAD EBOOK

This book focuses on the fields of hybrid intelligent systems based on fuzzy systems, neural networks, bio-inspired algorithms and time series. This book describes the construction of ensembles of Interval Type-2 Fuzzy Neural Networks models and the optimization of their fuzzy integrators with bio-inspired algorithms for time series prediction. Interval type-2 and type-1 fuzzy systems are used to integrate the outputs of the Ensemble of Interval Type-2 Fuzzy Neural Network models. Genetic Algorithms and Particle Swarm Optimization are the Bio-Inspired algorithms used for the optimization of the fuzzy response integrators. The Mackey-Glass, Mexican Stock Exchange, Dow Jones and NASDAQ time series are used to test of performance of the proposed method. Prediction errors are evaluated by the following metrics: Mean Absolute Error, Mean Square Error, Root Mean Square Error, Mean Percentage Error and Mean Absolute Percentage Error. The proposed prediction model outperforms state of the art methods in predicting the particular time series considered in this work.

Recent Advances in Interval Type-2 Fuzzy Systems
Recent Advances in Interval Type-2 Fuzzy Systems

Author: Oscar Castillo

Publisher: Springer Science & Business Media

Published: 2012-04-23

Total Pages: 93

ISBN-13: 3642289568

DOWNLOAD EBOOK

This book reviews current state of the art methods for building intelligent systems using type-2 fuzzy logic and bio-inspired optimization techniques. Combining type-2 fuzzy logic with optimization algorithms, powerful hybrid intelligent systems have been built using the advantages that each technique offers. This book is intended to be a reference for scientists and engineers interested in applying type-2 fuzzy logic for solving problems in pattern recognition, intelligent control, intelligent manufacturing, robotics and automation. This book can also be used as a reference for graduate courses like the following: soft computing, intelligent pattern recognition, computer vision, applied artificial intelligence, and similar ones. We consider that this book can also be used to get novel ideas for new lines of re-search, or to continue the lines of research proposed by the authors.

INTERVAL TYPE-2 FUZZY SETS AND INTERVAL NEUTROSOPHIC SETS IN INTELLIGENT SYSTEMS
INTERVAL TYPE-2 FUZZY SETS AND INTERVAL NEUTROSOPHIC SETS IN INTELLIGENT SYSTEMS

Author: M. LATHA MAHESWARI M.

Publisher: Infinite Study

Published:

Total Pages: 407

ISBN-13:

DOWNLOAD EBOOK

In this thesis, interval type-2 fuzzy sets (IT2FSs) and interval neutrosophic sets (INSs) have been considered for all the proposed concepts. Fusion of information is an essential task to get the optimized solution for any real world problem. In this task, aggregation operators are playing an important role in all the fields. Since most of the realistic problems have uncertainty in nature, one can use the logic of fuzzy and neutrosophic theory. For the entire proposed concepts interval based logic has been used as it handles more uncertainty.

Innovations in Bio-Inspired Computing and Applications
Innovations in Bio-Inspired Computing and Applications

Author: Ajith Abraham

Publisher: Springer

Published: 2018-03-09

Total Pages: 391

ISBN-13: 3319763547

DOWNLOAD EBOOK

This book highlights recent research results in Bio-Inspired Computing and Applications. It presents 33 selected papers from the 8th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA 2017), which was held in Marrakesh, Morocco from December 11 to 13, 2017. A premier conference in the nature-inspired computing field, IBICA is intended to bring together the world’s leading researchers and practitioners interested in advancing the state of the art in biologically inspired computing, allowing them to exchange notes on a broad range of disciplines. The book offers a valuable reference guide for all researchers, students and practitioners in the fields of Computer Science and Engineering.

Type-2 Fuzzy Logic in Intelligent Control Applications
Type-2 Fuzzy Logic in Intelligent Control Applications

Author: Oscar Castillo

Publisher: Springer

Published: 2011-11-08

Total Pages: 187

ISBN-13: 364224663X

DOWNLOAD EBOOK

We describe in this book, hybrid intelligent systems based mainly on type-2 fuzzy logic for intelligent control. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, and bio-inspired optimization algorithms, which can be used to produce powerful automatic control systems. The book is organized in three main parts, which contain a group of chapters around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which can be the basis for achieving intelligent control with interval type-2 fuzzy logic. The second part of the book is comprised of chapters with the main theme of evolutionary optimization of type-2 fuzzy systems in intelligent control with the aim of designing optimal type-2 fuzzy controllers for complex control problems in diverse areas of application, including mobile robotics, aircraft dynamics systems and hardware implementations. The third part of the book is formed with chapters dealing with the theme of bio-inspired optimization of type-2 fuzzy systems in intelligent control, which includes the application of particle swarm intelligence and ant colony optimization algorithms for obtaining optimal type-2 fuzzy controllers.

Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making
Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making

Author: Cengiz Kahraman

Publisher: Springer

Published: 2019-07-05

Total Pages: 1386

ISBN-13: 3030237567

DOWNLOAD EBOOK

This book includes the proceedings of the Intelligent and Fuzzy Techniques INFUS 2019 Conference, held in Istanbul, Turkey, on July 23–25, 2019. Big data analytics refers to the strategy of analyzing large volumes of data, or big data, gathered from a wide variety of sources, including social networks, videos, digital images, sensors, and sales transaction records. Big data analytics allows data scientists and various other users to evaluate large volumes of transaction data and other data sources that traditional business systems would be unable to tackle. Data-driven and knowledge-driven approaches and techniques have been widely used in intelligent decision-making, and they are increasingly attracting attention due to their importance and effectiveness in addressing uncertainty and incompleteness. INFUS 2019 focused on intelligent and fuzzy systems with applications in big data analytics and decision-making, providing an international forum that brought together those actively involved in areas of interest to data science and knowledge engineering. These proceeding feature about 150 peer-reviewed papers from countries such as China, Iran, Turkey, Malaysia, India, USA, Spain, France, Poland, Mexico, Bulgaria, Algeria, Pakistan, Australia, Lebanon, and Czech Republic.

General Type-2 Fuzzy Logic in Dynamic Parameter Adaptation for the Harmony Search Algorithm
General Type-2 Fuzzy Logic in Dynamic Parameter Adaptation for the Harmony Search Algorithm

Author: Fevrier Valdez

Publisher: Springer Nature

Published: 2020-03-27

Total Pages: 86

ISBN-13: 303043950X

DOWNLOAD EBOOK

This book focuses on the fields of fuzzy logic and metaheuristic algorithms, particularly the harmony search algorithm and fuzzy control. There are currently several types of metaheuristics used to solve a range of real-world of problems, and these metaheuristics contain parameters that are usually fixed throughout the iterations. However, a number of techniques are also available that dynamically adjust the parameters of an algorithm, such as probabilistic fuzzy logic. This book proposes a method of addressing the problem of parameter adaptation in the original harmony search algorithm using type-1, interval type-2 and generalized type-2 fuzzy logic. The authors applied this methodology to the resolution of problems of classical benchmark mathematical functions, CEC 2015, CEC2017 functions and to the optimization of various fuzzy logic control cases, and tested the method using six benchmark control problems – four of the Mamdani type: the problem of filling a water tank, the problem of controlling the temperature of a shower, the problem of controlling the trajectory of an autonomous mobile robot and the problem of controlling the speed of an engine; and two of the Sugeno type: the problem of controlling the balance of a bar and ball, and the problem of controlling control the balance of an inverted pendulum. When the interval type-2 fuzzy logic system is used to model the behavior of the systems, the results show better stabilization because the uncertainty analysis is better. As such, the authors conclude that the proposed method, based on fuzzy systems, fuzzy controllers and the harmony search optimization algorithm, improves the behavior of complex control plants.