Advanced topics on cellular self-organizing nets and chaotic nonlinear dynamics to model and control complex systems:
Gespeichert in:
| Format: | Elektronisch E-Book |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Singapore
World Scientific
©2008
|
| Schriftenreihe: | World Scientific series on nonlinear science
v. 63 |
| Schlagworte: | |
| Online-Zugang: | FAW01 FAW02 Volltext |
| Beschreibung: | Includes bibliographical references and index 1. The CNN paradigm for complexity. 1.1. Introduction. 1.2. The 3D-CNN model. 1.3. E[symbol]: an universal emulator for complex systems. 1.4. Emergence of forms in 3D-CNNs. 1.5. Conclusions -- 2. Emergent phenomena in neuroscience. 2.1. Introductory material: neurons and models. 2.2. Electronic implementation of neuron models. 2.3. Local activity theory for systems of IO neurons. 2.4. Simulation of IO systems: emerging results. 2.5. Networks of HR neurons. 2.6. Neurons in presence of noise. 2.7. Conclusions -- 3. Frequency analysis and identification in atomic force microscopy. 3.1. Introduction. 3.2. AFM modeling. 3.3. Frequency analysis via harmonic balance. 3.4. Identification of the tip-sample force model. 3.5. Conclusions -- 4. Control and parameter estimation of systems with low-dimensional chaos -- the role of peak-to-peak dynamics. 4.1. Introduction. 4.2. Peak-to-peak dynamics. 4.3. Control system design. 4.4. Parameter estimation. 4.5. Concluding remarks -- 5. Synchronization of complex networks. 5.1. Introduction. 5.2. Synchronization of interacting oscillators. 5.3. From local to long-range connections. 5.4. The master stability function. 5.5. Key elements for the assessing of synchronizability. 5.6. Synchronizability of weighted networks. 5.7. Synchronization of coupled oscillators: some significant results. 5.8. Conclusions -- 6. Economic sector identification in a set of stocks traded at the New York Exchange: a comparative analysis. 6.1. Introduction. 6.2. The data set. 6.3. Random matrix theory. 6.4. Hierarchical clustering methods. 6.5. The planar maximally filtered graph. 6.6. Conclusions -- 7. Innovation systems by nonlinear networks. 7.1. Introduction. 7.2. Cellular automata model. 7.3. Innovation models based on CNNs. 7.4. Simulation results. 7.5. Conclusions This book focuses on the research topics investigated during the three-year research project funded by the Italian Ministero dell'Istruzione, dell'Universitè e della Ricerca (MIUR: Ministry of Education, University and Research) under the FIRB project RBNE01CW3M. With the aim of introducing newer perspectives of the research on complexity, the final results of the project are presented after a general introduction to the subject. The book is intended to provide researchers, PhD students, and people involved in research projects in companies with the basic fundamentals of complex systems and the advanced project results recently obtained |
| Beschreibung: | 1 Online-Ressource (xvi, 191 pages) |
| ISBN: | 9789812814043 9789812814050 9812814051 |
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| 490 | 0 | |a World Scientific series on nonlinear science |v v. 63 | |
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| 500 | |a 1. The CNN paradigm for complexity. 1.1. Introduction. 1.2. The 3D-CNN model. 1.3. E[symbol]: an universal emulator for complex systems. 1.4. Emergence of forms in 3D-CNNs. 1.5. Conclusions -- 2. Emergent phenomena in neuroscience. 2.1. Introductory material: neurons and models. 2.2. Electronic implementation of neuron models. 2.3. Local activity theory for systems of IO neurons. 2.4. Simulation of IO systems: emerging results. 2.5. Networks of HR neurons. 2.6. Neurons in presence of noise. 2.7. Conclusions -- 3. Frequency analysis and identification in atomic force microscopy. 3.1. Introduction. 3.2. AFM modeling. 3.3. Frequency analysis via harmonic balance. 3.4. Identification of the tip-sample force model. 3.5. Conclusions -- 4. Control and parameter estimation of systems with low-dimensional chaos -- the role of peak-to-peak dynamics. 4.1. Introduction. 4.2. Peak-to-peak dynamics. 4.3. Control system design. 4.4. Parameter estimation. 4.5. Concluding remarks -- 5. Synchronization of complex networks. 5.1. Introduction. 5.2. Synchronization of interacting oscillators. 5.3. From local to long-range connections. 5.4. The master stability function. 5.5. Key elements for the assessing of synchronizability. 5.6. Synchronizability of weighted networks. 5.7. Synchronization of coupled oscillators: some significant results. 5.8. Conclusions -- 6. Economic sector identification in a set of stocks traded at the New York Exchange: a comparative analysis. 6.1. Introduction. 6.2. The data set. 6.3. Random matrix theory. 6.4. Hierarchical clustering methods. 6.5. The planar maximally filtered graph. 6.6. Conclusions -- 7. Innovation systems by nonlinear networks. 7.1. Introduction. 7.2. Cellular automata model. 7.3. Innovation models based on CNNs. 7.4. Simulation results. 7.5. Conclusions | ||
| 500 | |a This book focuses on the research topics investigated during the three-year research project funded by the Italian Ministero dell'Istruzione, dell'Universitè e della Ricerca (MIUR: Ministry of Education, University and Research) under the FIRB project RBNE01CW3M. With the aim of introducing newer perspectives of the research on complexity, the final results of the project are presented after a general introduction to the subject. The book is intended to provide researchers, PhD students, and people involved in research projects in companies with the basic fundamentals of complex systems and the advanced project results recently obtained | ||
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| 650 | 4 | |a Self-organizing maps | |
| 650 | 4 | |a System theory |x Mathematical models | |
| 700 | 1 | |a Caponetto, R. |e Sonstige |4 oth | |
| 700 | 1 | |a Fortuna, L. |e Sonstige |4 oth | |
| 700 | 1 | |a Frasca, Mattia |e Sonstige |4 oth | |
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Datensatz im Suchindex
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| dewey-raw | 511.3 22 |
| dewey-search | 511.3 22 |
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| id | DE-604.BV043081407 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:16:51Z |
| institution | BVB |
| isbn | 9789812814043 9789812814050 9812814051 |
| language | English |
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| series2 | World Scientific series on nonlinear science |
| spelling | Advanced topics on cellular self-organizing nets and chaotic nonlinear dynamics to model and control complex systems edited by Riccardo Caponetto, Luigi Fortuna, Mattia Frasca Singapore World Scientific ©2008 1 Online-Ressource (xvi, 191 pages) txt rdacontent c rdamedia cr rdacarrier World Scientific series on nonlinear science v. 63 Includes bibliographical references and index 1. The CNN paradigm for complexity. 1.1. Introduction. 1.2. The 3D-CNN model. 1.3. E[symbol]: an universal emulator for complex systems. 1.4. Emergence of forms in 3D-CNNs. 1.5. Conclusions -- 2. Emergent phenomena in neuroscience. 2.1. Introductory material: neurons and models. 2.2. Electronic implementation of neuron models. 2.3. Local activity theory for systems of IO neurons. 2.4. Simulation of IO systems: emerging results. 2.5. Networks of HR neurons. 2.6. Neurons in presence of noise. 2.7. Conclusions -- 3. Frequency analysis and identification in atomic force microscopy. 3.1. Introduction. 3.2. AFM modeling. 3.3. Frequency analysis via harmonic balance. 3.4. Identification of the tip-sample force model. 3.5. Conclusions -- 4. Control and parameter estimation of systems with low-dimensional chaos -- the role of peak-to-peak dynamics. 4.1. Introduction. 4.2. Peak-to-peak dynamics. 4.3. Control system design. 4.4. Parameter estimation. 4.5. Concluding remarks -- 5. Synchronization of complex networks. 5.1. Introduction. 5.2. Synchronization of interacting oscillators. 5.3. From local to long-range connections. 5.4. The master stability function. 5.5. Key elements for the assessing of synchronizability. 5.6. Synchronizability of weighted networks. 5.7. Synchronization of coupled oscillators: some significant results. 5.8. Conclusions -- 6. Economic sector identification in a set of stocks traded at the New York Exchange: a comparative analysis. 6.1. Introduction. 6.2. The data set. 6.3. Random matrix theory. 6.4. Hierarchical clustering methods. 6.5. The planar maximally filtered graph. 6.6. Conclusions -- 7. Innovation systems by nonlinear networks. 7.1. Introduction. 7.2. Cellular automata model. 7.3. Innovation models based on CNNs. 7.4. Simulation results. 7.5. Conclusions This book focuses on the research topics investigated during the three-year research project funded by the Italian Ministero dell'Istruzione, dell'Universitè e della Ricerca (MIUR: Ministry of Education, University and Research) under the FIRB project RBNE01CW3M. With the aim of introducing newer perspectives of the research on complexity, the final results of the project are presented after a general introduction to the subject. The book is intended to provide researchers, PhD students, and people involved in research projects in companies with the basic fundamentals of complex systems and the advanced project results recently obtained MATHEMATICS / Set Theory bisacsh Computational complexity fast Nonlinear systems / Mathematical models fast Self-organizing maps fast System theory / Mathematical models fast Mathematisches Modell Computational complexity Nonlinear systems Mathematical models Self-organizing maps System theory Mathematical models Caponetto, R. Sonstige oth Fortuna, L. Sonstige oth Frasca, Mattia Sonstige oth http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=521160 Aggregator Volltext |
| spellingShingle | Advanced topics on cellular self-organizing nets and chaotic nonlinear dynamics to model and control complex systems MATHEMATICS / Set Theory bisacsh Computational complexity fast Nonlinear systems / Mathematical models fast Self-organizing maps fast System theory / Mathematical models fast Mathematisches Modell Computational complexity Nonlinear systems Mathematical models Self-organizing maps System theory Mathematical models |
| title | Advanced topics on cellular self-organizing nets and chaotic nonlinear dynamics to model and control complex systems |
| title_auth | Advanced topics on cellular self-organizing nets and chaotic nonlinear dynamics to model and control complex systems |
| title_exact_search | Advanced topics on cellular self-organizing nets and chaotic nonlinear dynamics to model and control complex systems |
| title_full | Advanced topics on cellular self-organizing nets and chaotic nonlinear dynamics to model and control complex systems edited by Riccardo Caponetto, Luigi Fortuna, Mattia Frasca |
| title_fullStr | Advanced topics on cellular self-organizing nets and chaotic nonlinear dynamics to model and control complex systems edited by Riccardo Caponetto, Luigi Fortuna, Mattia Frasca |
| title_full_unstemmed | Advanced topics on cellular self-organizing nets and chaotic nonlinear dynamics to model and control complex systems edited by Riccardo Caponetto, Luigi Fortuna, Mattia Frasca |
| title_short | Advanced topics on cellular self-organizing nets and chaotic nonlinear dynamics to model and control complex systems |
| title_sort | advanced topics on cellular self organizing nets and chaotic nonlinear dynamics to model and control complex systems |
| topic | MATHEMATICS / Set Theory bisacsh Computational complexity fast Nonlinear systems / Mathematical models fast Self-organizing maps fast System theory / Mathematical models fast Mathematisches Modell Computational complexity Nonlinear systems Mathematical models Self-organizing maps System theory Mathematical models |
| topic_facet | MATHEMATICS / Set Theory Computational complexity Nonlinear systems / Mathematical models Self-organizing maps System theory / Mathematical models Mathematisches Modell Nonlinear systems Mathematical models System theory Mathematical models |
| url | http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=521160 |
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