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Type-3 fuzzy logic in time series prediction

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Type-3 fuzzy logic in time series prediction/ by Oscar Castillo, Patricia Melin.
作者:	Castillo, Oscar.
其他作者:	Melin, Patricia.
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	viii, 99 p. :ill. (chiefly col.), digital ;24 cm.
內容註:	Chapter1. Type-3 Fuzzy Prediction -- Chapter 2. Type-3 for Prediction -- Chapter 3. Type-3 Fuzzy Logic in Time Series Prediction -- Chapter 4. Prediction with a Hybrid Interval Type-3 Fuzzy-Fractal Approach -- Chapter 5. Type-3 Fuzzy Aggregation of Neural Networks.
Contained By:	Springer Nature eBook
標題:	Time-series analysis. -
電子資源:	https://doi.org/10.1007/978-3-031-59714-5
ISBN:	9783031597145

Type-3 fuzzy logic in time series prediction
Castillo, Oscar.

Type-3 fuzzy logic in time series prediction[electronic resource] /by Oscar Castillo, Patricia Melin. - Cham :Springer Nature Switzerland :2024. - viii, 99 p. :ill. (chiefly col.), digital ;24 cm. - SpringerBriefs in applied sciences and technology. Computational intelligence,2625-3712. - SpringerBriefs in applied sciences and technology.Computational intelligence..

Chapter1. Type-3 Fuzzy Prediction -- Chapter 2. Type-3 for Prediction -- Chapter 3. Type-3 Fuzzy Logic in Time Series Prediction -- Chapter 4. Prediction with a Hybrid Interval Type-3 Fuzzy-Fractal Approach -- Chapter 5. Type-3 Fuzzy Aggregation of Neural Networks.

This book focuses on the field of type-3 fuzzy logic for applications in time series prediction. The main idea is that a higher type and order of fuzzy logic can help in solving various prediction problems and find better results. In addition, neural networks and fractal theory are employed in enhancing prediction results. In this regard, several hybrid intelligent methods are offered. In this book we test the proposed methods using several prediction problems, like predicting COVID-19 and the stock market. We can notice that when Type-3 fuzzy systems are implemented to model the behavior of systems, the results in prediction are enhanced, because the management of uncertainty is better. For this reason, we consider in this book the proposed methods using type-3 fuzzy systems, neural networks and fractal theory to improve the prediction behavior of the complex nonlinear systems. This book is intended to be a reference for scientists and engineers interested in applying type-3 fuzzy logic techniques for solving complex prediction problems. This book can also be used as a reference for graduate courses like the following: soft computing, fuzzy logic, neural networks, bio-inspired algorithms, intelligent prediction, and similar ones. We consider that this book can also be used to get novel ideas for new lines of research, or to continue the lines of research proposed by the authors of the book.

ISBN: 9783031597145

Standard No.: 10.1007/978-3-031-59714-5doiSubjects--Topical Terms:

532530
Time-series analysis.

LC Class. No.: QA280

Dewey Class. No.: 519.55

Type-3 fuzzy logic in time series prediction
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520 $a This book focuses on the field of type-3 fuzzy logic for applications in time series prediction. The main idea is that a higher type and order of fuzzy logic can help in solving various prediction problems and find better results. In addition, neural networks and fractal theory are employed in enhancing prediction results. In this regard, several hybrid intelligent methods are offered. In this book we test the proposed methods using several prediction problems, like predicting COVID-19 and the stock market. We can notice that when Type-3 fuzzy systems are implemented to model the behavior of systems, the results in prediction are enhanced, because the management of uncertainty is better. For this reason, we consider in this book the proposed methods using type-3 fuzzy systems, neural networks and fractal theory to improve the prediction behavior of the complex nonlinear systems. This book is intended to be a reference for scientists and engineers interested in applying type-3 fuzzy logic techniques for solving complex prediction problems. This book can also be used as a reference for graduate courses like the following: soft computing, fuzzy logic, neural networks, bio-inspired algorithms, intelligent prediction, and similar ones. We consider that this book can also be used to get novel ideas for new lines of research, or to continue the lines of research proposed by the authors of the book.
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