LEADER 05884nam 2200469 450 001 9910627243803321 005 20230228115010.0 010 $a9783031164002$b(electronic bk.) 010 $z9783031163999 035 $a(MiAaPQ)EBC7103009 035 $a(Au-PeEL)EBL7103009 035 $a(CKB)24963271300041 035 $a(PPN)265856302 035 $a(EXLCZ)9924963271300041 100 $a20230228d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBanach space valued neural network $eordinary and fractional approximation and interpolation /$fGeorge A. Anastassiou 210 1$aCham, Switzerland :$cSpringer International Publishing,$d[2022] 210 4$dİ2022 215 $a1 online resource (429 pages) 225 1 $aStudies in Computational Intelligence 311 08$aPrint version: Anastassiou, George A. Banach Space Valued Neural Network Cham : Springer International Publishing AG,c2022 9783031163999 327 $aIntro -- Preface -- Contents -- 1 Algebraic Function Induced Banach Space Valued Ordinary and Fractional Neural Network Approximations -- 1.1 Introduction -- 1.2 Basics -- 1.3 Main Results -- References -- 2 Gudermannian Function Induced Banach Space Valued Ordinary and Fractional Neural Network Approximations -- 2.1 Introduction -- 2.2 Basics -- 2.3 Main Results -- References -- 3 Generalized Symmetrical Sigmoid Function Induced Banach Space Valued Ordinary and Fractional Neural Network Approximations -- 3.1 Introduction -- 3.2 Auxiliary Results -- 3.3 Main Results -- References -- 4 Abstract Multivariate Algebraic Function Induced Neural Network Approximations -- 4.1 Introduction -- 4.2 Basic -- 4.3 Multivariate General Neural Network Approximations -- References -- 5 General Multivariate Arctangent Function Induced Neural Network Approximations -- 5.1 Introduction -- 5.2 Auxiliary Notions -- 5.3 Multivariate General Neural Network Approximations -- References -- 6 Abstract Multivariate Gudermannian Function Induced Neural Network Approximations -- 6.1 Introduction -- 6.2 Background -- 6.3 Multivariate General Neural Network Approximations -- References -- 7 Generalized Symmetrical Sigmoid Function Induced Neural Network Multivariate Approximation -- 7.1 Introduction -- 7.2 Auxiliary Results (See Also ch77.14) -- 7.3 Multivariate General Neural Network Approximations -- References -- 8 Quantitative Approximation by Kantorovich-Choquet Quasi-Interpolation Neural Network Operators Revisited -- 8.1 Introduction -- 8.2 Background -- 8.2.1 About the Arctangent Activation Function -- 8.2.2 About the Algebraic Activation Function -- 8.2.3 About the Gudermannian Activation Function -- 8.2.4 About the Generalized Symmetrical Activation Function -- 8.3 Main Results -- References. 327 $a9 Quantitative Approximation by Kantorovich-Shilkret Quasi-interpolation Neural Network Operators Revisited -- 9.1 Introduction -- 9.2 Background -- 9.2.1 About the Arctangent Activation Function -- 9.2.2 About the Algebraic Activation Function -- 9.2.3 About the Gudermannian Activation Function -- 9.2.4 About the Generalized Symmetrical Activation Function -- 9.3 Main Results -- References -- 10 Voronouskaya Univariate and Multivariate Asymptotic Expansions for Sigmoid Functions Induced Quasi-interpolation Neural Network Operators Revisited -- 10.1 Background -- 10.1.1 About the Arctangent Activation Function -- 10.1.2 About the Algebraic Activation Function -- 10.1.3 About the Gudermannian Activation Function -- 10.1.4 About the Generalized Symmetrical Activation Function -- 10.2 Main Results -- References -- 11 Univariate Fuzzy Fractional Various Sigmoid Function Activated Neural Network Approximations Revisited -- 11.1 Introduction -- 11.2 Fuzzy Fractional Mathematical Analysis Basics -- 11.3 Real Neural Network Approximation -- 11.3.1 About the Arctangent Activation Function Neural Networks -- 11.3.2 About the Algebraic Activation Function Neural Networks -- 11.3.3 About the Gudermannian Activation Function Neural Networks -- 11.3.4 About the Generalized Symmetrical Activation Function Neural Networks -- 11.4 Main Results: Approximation by Fuzzy Quasi-interpolation Neural ? -- References -- 12 Multivariate Fuzzy Approximation by Neural Network Operators Induced by Several Sigmoid Functions Revisited -- 12.1 Introduction -- 12.2 Fuzzy Real Analysis Background -- 12.3 About Neural Networks Background -- 12.3.1 About the Arctangent Activation Function -- 12.3.2 About the Algebraic Activation Function -- 12.3.3 About the Gudermannian Activation Function -- 12.3.4 About the Generalized Symmetrical Activation Function. 327 $a12.4 Main Results: Fuzzy Multivariate Neural Network Approximation Based ? -- References -- 13 Multivariate Fuzzy-Random and Stochastic Various Activation Functions Activated Neural Network Approximations -- 13.1 Fuzzy-Random Functions and Stochastic Processes Background -- 13.2 About Neural Networks Background -- 13.2.1 About the Arctangent Activation Function -- 13.2.2 About the Algebraic Activation Function -- 13.2.3 About the Gudermannian Activation Function -- 13.2.4 About the Generalized Symmetrical Activation Function -- 13.3 Main Results -- References -- Appendix Conclusion. 410 0$aStudies in computational intelligence. 606 $aNeural networks (Computer science) 606 $aNeural networks (Computer science)$xDesign and construction 615 0$aNeural networks (Computer science) 615 0$aNeural networks (Computer science)$xDesign and construction. 676 $a006.32 700 $aAnastassiou$b George A.$060024 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910627243803321 996 $aBanach Space Valued Neural Network$92975465 997 $aUNINA LEADER 04561nam 2201273z- 450 001 9910557539303321 005 20220111 035 $a(CKB)5400000000044214 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76780 035 $a(oapen)doab76780 035 $a(EXLCZ)995400000000044214 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aCFD Modeling of Complex Chemical Processes: Multiscale and Multiphysics Challenges 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (296 p.) 311 08$a3-0365-1266-7 311 08$a3-0365-1267-5 330 $aComputational fluid dynamics (CFD), which uses numerical analysis to predict and model complex flow behaviors and transport processes, has become a mainstream tool in engineering process research and development. Complex chemical processes often involve coupling between dynamics at vastly different length and time scales, as well as coupling of different physical models. The multiscale and multiphysics nature of those problems calls for delicate modeling approaches. This book showcases recent contributions in this field, from the development of modeling methodology to its application in supporting the design, development, and optimization of engineering processes. 517 $aCFD Modeling of Complex Chemical Processes 606 $aTechnology: general issues$2bicssc 610 $aauxiliary ventilation 610 $abinary mixture 610 $abioprocess engineering 610 $abioreactor 610 $acapillary rise 610 $aCFD 610 $acharge estimation 610 $acoal mining 610 $acombustion characteristics 610 $acomputational fluid dynamics 610 $aconcentration gradients 610 $aconcentration polarization 610 $acyclone separator 610 $adigital twin 610 $aDPM 610 $adroplet characteristic 610 $adual-impeller 610 $aduct position 610 $adynamic numerical simulation 610 $aelevated temperature process 610 $aEuler-Lagrange approach 610 $aEulerian-Lagrangian approach 610 $aevaporative cooling system 610 $afluidized bed 610 $agas separation 610 $agas-solid 610 $agasification 610 $aheat transport 610 $ahigh pressure bubble column 610 $ahumidity 610 $ahydrodynamics 610 $ain situ particle size measurement 610 $aindustrial pulverized coal furnace 610 $ainlet/outlet 610 $alarge coal particles 610 $amechanistic kinetic model 610 $amembrane module 610 $amulti-objective optimization process 610 $amultiphase flow 610 $an/a 610 $anatural gas desulfurization 610 $anumerical simulations 610 $aoccupational exposure assessment 610 $aOptimization 610 $aoptimized design 610 $aparticle charge 610 $aparticle size distribution 610 $apneumatic conveying 610 $apressure drop 610 $apumped hydroelectric storage 610 $aradon concentration 610 $arising bubbles 610 $arotating packed bed 610 $aSaccharomyces cerevisiae 610 $ascale-down 610 $ascale-up 610 $aSegment impeller 610 $aStirred fermenter 610 $asurface tension modelling 610 $asurrogate model selection 610 $aswirling burner 610 $atemperature 610 $athe critical bubble diameter 610 $athe gas holdup 610 $athe heat dissipation of LHD 610 $athe large bubbles 610 $athe small bubbles 610 $athermal environment 610 $atriboelectric separation 610 $aventilation 610 $aventilation cooling 610 $aVOF 610 $awater recycling 615 7$aTechnology: general issues 700 $aXi$b Li$4edt$01322921 702 $aYin$b De-Wei$4edt 702 $aPark$b Jae$4edt 702 $aXi$b Li$4oth 702 $aYin$b De-Wei$4oth 702 $aPark$b Jae$4oth 906 $aBOOK 912 $a9910557539303321 996 $aCFD Modeling of Complex Chemical Processes: Multiscale and Multiphysics Challenges$93035266 997 $aUNINA