03745nam a22005175i 4500 978-3-031-59714-5 DE-He213 20251030112041.0 cr nn 008mamaa 240514s2024 sz | s |||| 0|eng d 9783031597145 978-3-031-59714-5 10.1007/978-3-031-59714-5 doi Q342 UYQ bicssc COM004000 bisacsh UYQ thema Castillo, Oscar. author. aut http://id.loc.gov/vocabulary/relators/aut 66767 Type-3 Fuzzy Logic in Time Series Prediction [electronic resource] / by Oscar Castillo, Patricia Melin. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. VIII, 99 p. 73 illus., 67 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda SpringerBriefs in Computational Intelligence, 2625-3712 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. Computational intelligence. 64052 Engineering mathematics. 64145 Computational Intelligence. 64052 Engineering Mathematics. 64145 Melin, Patricia. author. (orcid)0000-0001-5798-1426 https://orcid.org/0000-0001-5798-1426 aut http://id.loc.gov/vocabulary/relators/aut 74912 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031597138 Printed edition: 9783031597152 SpringerBriefs in Computational Intelligence, 2625-3712 74073 https://doi.org/10.1007/978-3-031-59714-5 ZDB-2-INR ZDB-2-SXIT 59850 59850