LEADER 01715nam 2200493 450 001 9910743359903321 005 20231110220602.0 010 $a981-16-8243-7 010 $a981-16-8244-5 010 $a981-16-8244-5 035 $a(MiAaPQ)EBC6825100 035 $a(Au-PeEL)EBL6825100 035 $a(CKB)20106119600041 035 $a(PPN)259387533 035 $a(EXLCZ)9920106119600041 100 $a20220828d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aIterative learning control for equations with fractional derivatives and impulses /$fJinRong Wang, Shengda Liu, and Michal Fec?kan 210 1$aSingapore :$cSpringer,$d[2022] 210 4$d©2022 215 $a1 online resource (263 pages) 225 1 $aStudies in Systems, Decision and Control ;$vv.403 311 08$aPrint version: Wang, JinRong Iterative Learning Control for Equations with Fractional Derivatives and Impulses Singapore : Springer Singapore Pte. Limited,c2022 9789811682438 320 $aIncludes bibliographical references. 410 0$aStudies in Systems, Decision and Control 606 $aControl theory 606 $aIterative methods (Mathematics) 615 0$aControl theory. 615 0$aIterative methods (Mathematics) 676 $a517.5 700 $aWang$b JinRong$01051916 702 $aLiu$b Shengda 702 $aFec?kan$b Michal 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910743359903321 996 $aIterative learning control for equations with fractional derivatives and impulses$93560442 997 $aUNINA LEADER 04867nam 2200517Ia 450 001 9910974187103321 005 20251116153428.0 010 0 $a9780191526343 010 0 $a0191526347 035 $a(MiAaPQ)EBC7036473 035 $a(CKB)24235107000041 035 $a(MiAaPQ)EBC415743 035 $a(Au-PeEL)EBL415743 035 $a(CaPaEBR)ebr10271599 035 $a(CaONFJC)MIL116083 035 $a(OCoLC)476244652 035 $a(Au-PeEL)EBL7036473 035 $a(EXLCZ)9924235107000041 100 $a20061129d2007 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aScale-free networks $ecomplex webs in nature and technology /$fGuido Caldarelli 205 $a1st ed. 210 $aOxford $cOxford University Press$d2007 215 $axiv, 309 p. $cill 225 1 $aOxford Finance Ser. 320 $aIncludes bibliographical references and index. 327 $aIntro -- Contents -- I: DEFINITIONS AND METHODOLOGY -- 1. Introduction to graphs -- 1.1 Graphs, directed graphs, and weighted graphs -- 1.2 Trees -- 1.3 Vertex correlation, assortativity -- 1.4 Hierarchical properties of graphs -- 1.5 The properties of scale-free networks -- 2. Graph structures: communities -- 2.1 Introduction -- 2.2 Typical subgraphs, motifs -- 2.3 Classes of vertices -- 2.4 Centrality measures, betweenness, and robustness -- 2.5 Clustering detection, modularity -- 2.6 Communities in graphs -- 3. Scale-invariance -- 3.1 Geometrical scale-invariance: fractals -- 3.2 Measuring the fractal dimension -- 3.3 Scale-invariance and power laws -- 3.4 Plotting a power law -- 3.5 Scale-invariance in natural sciences -- 3.6 Scale-invariance in economics and in social sciences -- 4. The origin of power-law functions -- 4.1 Random walk, Laplace equation, and fractals -- 4.2 Power laws from minimization principles -- 4.3 Multiplicative processes and normal distribution -- 4.4 Preferential attachment, the Matthew effect -- 5. Graph generating models -- 5.1 Random graph model -- 5.2 The small-world model -- 5.3 The Barabási-Albert model -- 5.4 Modifications to the Barabási-Albert model -- 5.5 Copying models -- 5.6 Fitness based model -- 5.7 Graph from optimization principles -- II: EXAMPLES -- 6. Networks in the cell -- 6.1 Basic cell biology -- 6.2 Protein-protein interaction network -- 6.3 Metabolic pathways -- 6.4 Gene regulatory networks -- 7. Geophysical networks -- 7.1 Satellite images and digital elevation models -- 7.2 Geometrical scale invariance for river networks -- 7.3 Scaling relations for river networks -- 7.4 River networks models -- 7.5 River networks on Mars' surface -- 8. Ecological networks -- 8.1 Species and evolution -- 8.2 Food webs: a very particular case of network -- 8.3 Food web quantities. 327 $a8.4 Classifications of species -- 8.5 Yule process for taxonomies -- 9. Technological networks: Internet and WWW -- 9.1 The Internet protocols -- 9.2 The geography of the Internet -- 9.3 The autonomous systems -- 9.4 The scale-invariance in the Internet -- 9.5 The World Wide Web -- 9.6 Searching the web -- 9.7 Statistical measures of the Web -- 9.8 E-mail networks -- 10. Social and cognitive networks -- 10.1 Networks of scientific papers -- 10.2 Contact networks -- 10.3 Linguistic networks -- 10.4 Wikipedia -- 11. Financial networks -- 11.1 Board of directors -- 11.2 Stock networks -- 11.3 Bank networks -- 11.4 The world trade web -- III: APPENDICES -- A. Glossary -- A -- B -- C -- D -- E -- F -- G -- H -- I -- L -- M -- N -- O -- P -- R -- S -- T -- V -- W -- B. Graph quantities -- B.1 Basics -- B.2 Different kinds of graphs -- B.3 Paths, cycles, and trees -- C. Basic statistics -- C.1 Events and probability -- C.2 Probability densities and distributions -- C.3 Working with statistical distributions -- C.4 Statistical properties of weighted networks -- D. Matrices and eigenvectors -- E. Population dynamics -- E.1 Population dynamics -- Bibliography -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- I -- K -- L -- M -- N -- O -- P -- R -- S -- T -- V -- W -- Y -- Z. 330 $aMany different systems both in nature and in technology can be described by means of networks of interconnected components. Despite their different aspects, all of them share similar mathematical properties. In this book we explain how to recognize these features and why these different systems develop this common structure. 410 0$aOxford Finance Ser. 606 $aSystem analysis 606 $aSystem theory 615 0$aSystem analysis. 615 0$aSystem theory. 676 $a003 700 $aCaldarelli$b Guido$0310392 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910974187103321 996 $aScale-free networks$91749546 997 $aUNINA