Advanced topics on cellular self-organizing nets and chaotic nonlinear dynamics to model and control complex systems /

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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 introduci...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Caponetto, R. (Riccardo), 1966-, Fortuna, L. (Luigi), 1953-, Frasca, Mattia
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Singapore : World Scientific, ©2008.
Colección:World Scientific series on nonlinear science. Monographs and treatises ; v. 63.
Temas:
Computational complexity.
Nonlinear systems > Mathematical models.
Self-organizing maps.
System theory > Mathematical models.
Complexité de calcul (Informatique)
Systèmes non linéaires > Modèles mathématiques.
Cartes auto-organisatrices.
Théorie des systèmes > Modèles mathématiques.
MATHEMATICS > Set Theory.
Computational complexity
Nonlinear systems > Mathematical models
Self-organizing maps
System theory > Mathematical models
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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.

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