LEADER 03500nam 22005895 450 001 9910857794703321 005 20240514130329.0 010 $a9783031597145 010 $a3031597141 024 7 $a10.1007/978-3-031-59714-5 035 $a(CKB)32027758100041 035 $a(MiAaPQ)EBC31342690 035 $a(Au-PeEL)EBL31342690 035 $a(MiAaPQ)EBC31339990 035 $a(Au-PeEL)EBL31339990 035 $a(OCoLC)1434176679 035 $a(DE-He213)978-3-031-59714-5 035 $a(EXLCZ)9932027758100041 100 $a20240514d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aType-3 Fuzzy Logic in Time Series Prediction /$fby Oscar Castillo, Patricia Melin 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (102 pages) 225 1 $aSpringerBriefs in Computational Intelligence,$x2625-3712 311 08$a9783031597138 311 08$a3031597133 327 $aChapter1. 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. 330 $aThis book focuses on the field of type-3 fuzzy logic for applications in time series prediction. The main idea is that a higher type and order of fuzzy logic can help in solving various prediction problems and find better results. In addition, neural networks and fractal theory are employed in enhancing prediction results. In this regard, several hybrid intelligent methods are offered. In this book we test the proposed methods using several prediction problems, like predicting COVID-19 and the stock market. We can notice that when Type-3 fuzzy systems are implemented to model the behavior of systems, the results in prediction are enhanced, because the management of uncertainty is better. For this reason, we consider in this book the proposed methods using type-3 fuzzy systems, neural networks and fractal theory to improve the prediction behavior of the complex nonlinear systems. This book is intended to be a reference for scientists and engineers interested in applying type-3 fuzzy logic techniques for solving complex prediction problems. This book can also be used as a reference for graduate courses like the following: soft computing, fuzzy logic, neural networks, bio-inspired algorithms, intelligent prediction, and similar ones. We consider that this book can also be used to get novel ideas for new lines of research, or to continue the lines of research proposed by the authors of the book. 410 0$aSpringerBriefs in Computational Intelligence,$x2625-3712 606 $aComputational intelligence 606 $aEngineering mathematics 606 $aComputational Intelligence 606 $aEngineering Mathematics 615 0$aComputational intelligence. 615 0$aEngineering mathematics. 615 14$aComputational Intelligence. 615 24$aEngineering Mathematics. 676 $a006.3 700 $aCastillo$b Oscar$0762265 701 $aMelin$b Patricia$0762263 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910857794703321 996 $aType-3 Fuzzy Logic in Time Series Prediction$94161300 997 $aUNINA