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Title 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
Published Singapore : World Scientific, ©2008


Description 1 online resource (xvi, 191 pages) : illustrations (some color)
Series World Scientific series on nonlinear science. Series A, Monographs and treatises ; v. 63
World Scientific series on nonlinear science. Series A, Monographs and treatises ; v. 63.
Contents 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
Summary 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
Bibliography Includes bibliographical references and index
Notes Print version record
Subject Computational complexity.
Nonlinear systems -- Mathematical models
Self-organizing maps.
System theory -- Mathematical models
MATHEMATICS -- Set Theory.
Computational complexity
Nonlinear systems -- Mathematical models
Self-organizing maps
System theory -- Mathematical models
Form Electronic book
Author Caponetto, R. (Riccardo), 1966-
Fortuna, L. (Luigi), 1953-
Frasca, Mattia
LC no. 2008301004
ISBN 9812814051