Advanced topics on cellular self-organizing nets and chaotic nonlinear dynamics to model and control complex systems
This book focuses on the research topics investigated during the three-year research project funded by the Italian Ministero dell'Istruzione, dell'Università e della Ricerca (MIUR: Ministry of Education, University and Research) under the FIRB project RBNE01CW3M. With the aim of introducin...
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| Main Authors | , , |
|---|---|
| Format | eBook Book |
| Language | English |
| Published |
New Jersey
World Scientific Publishing Co. Pte. Ltd
2008
World Scientific World Scientific Publishing Company WORLD SCIENTIFIC WSPC |
| Edition | 1 |
| Series | Nonlinear Science, Series A: Monographs and Treatises |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9789812814043 9812814043 9812814051 9789812814050 |
| DOI | 10.1142/6830 |
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| Abstract | This book focuses on the research topics investigated during the three-year research project funded by the Italian Ministero dell'Istruzione, dell'Università 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. |
|---|---|
| AbstractList | This book focuses on the research topics investigated during the three-year research project funded by the Italian Ministero dell'Istruzione, dell'Università 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.Sample Chapter(s)Chapter 1: The CNN Paradigm for Complexity (2,602 KB)Contents: The CNN Paradigm for ComplexityEmergent Phenomena in NeuroscienceFrequency Analysis and Identification in Atomic Force MicroscopyControl and Parameter Estimation of Systems with Low-Dimensional Chaos - The Role of Peak-to-Peak DynamicsSynchronization of Complex NetworksEconomic Sector Identification in a Set of Stocks Traded at the New York Exchange: A Comparative AnalysisInnovation Systems by Nonlinear NetworksReadership: Advanced undergraduate and graduate students in electronic and automation engineering, physicists, engineers and non-experts interested in complex systems. This book focuses on the research topics investigated during the three-year research project funded by the Italian Ministero dell'Istruzione, dell'Università 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. Sample Chapter(s) This book focuses on the research topics investigated during the three-year research project funded by the Italian Ministero dell'Istruzione, dell'Università 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. |
| Author | Frasca, Mattia Caponetto, Riccardo Fortuna, Luigi |
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| Editor | Frasca, Mattia Caponetto, Riccardo Fortuna, Luigi |
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| Keywords | Automatic Control Self-Organization Complex Systems Analog Circuits Complex Networks Nonlinear Dynamics Cellular Nonlinear Networks |
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| SubjectTerms | Artificial Intelligence (Machine Learning, Neural Networks, Fuzzy Logic) Computational complexity Econometrics Game Theory General Physics Industrial and Systems Engineering Mathematical Economics Nonlinear Science, Chaos & Dynamical Systems Nonlinear systems Pure Mathematics Robotics (Machine Vision, Pattern Recognition) SCIENCE Self-organizing maps System theory |
| SubjectTermsDisplay | Computational complexity Nonlinear systems Self-organizing maps System theory |
| TableOfContents | Advanced topics on cellular self-organizing nets and chaotic nonlinear dynamics to model and control complex systems -- Preface -- Contributors -- List of People Involved in the FIRB Project -- Contents -- Chapter 1: The CNN Paradigm for Complexity -- Chapter 2: Emergent Phenomena in Neuroscience -- Chapter 3: Frequency Analysis and Identification in Atomic Force Microscopy -- Chapter 4: Control and Parameter Estimation of Systems with Low-Dimensional Chaos - The Role of Peak-to-Peak Dynamics -- Chapter 5: Synchronization of Complex Networks -- Chapter 6: Economic Sector Identification in a Set of Stocks Traded at the New York Exchange: A Comparative Analysis -- Chapter 7: Innovation Systems by Nonlinear Networks -- Index. 3.3 Frequency Analysis via Harmonic Balance -- 3.3.1 Piecewise interaction model analysis -- 3.3.2 Lennard Jones-like hysteretic model analysis -- 3.4 Identification of the Tip-Sample Force Model -- 3.4.1 Identification method -- 3.4.2 Experimental results -- 3.5 Conclusions -- References -- 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 -- 4.3.1 PPD modeling and control -- 4.3.2 The impact of noise and sampling frequency -- 4.3.3 PPD reconstruction -- 4.4 Parameter Estimation -- 4.4.1 Derivation of the "empirical PPP" -- 4.4.2 Interpolation of the "empirical PPP" -- 4.4.3 Optimization -- 4.4.4 Example of application -- 4.5 Concluding Remarks -- References -- 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 -- 5.4.1 The case of continuous time systems -- 5.4.2 The Master stability function for coupled maps -- 5.5 Key Elements for the Assessing of Synchronizability -- 5.5.1 Bounding the eigenratio -- 5.5.2 Other approaches for assessing synchronizability -- 5.6 Synchronizability of Weighted Networks -- 5.6.1 Coupling matrices with a real spectra -- 5.6.2 Numerical simulations -- 5.6.3 Weighting: local vs global approaches -- 5.6.4 Coupling matrices with a complex spectra -- 5.6.5 Essential topological features for synchronizability -- 5.7 Synchronization of Coupled Oscillators: Some Significant Results -- 5.7.1 Networks of phase oscillators -- 5.7.2 Networks of coupled oscillators -- 5.8 Conclusions -- References -- 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 -- 6.4.1 Single linkage correlation based clustering -- 6.4.2 Average linkage correlation based clustering -- 6.5 The Planar Maximally Filtered Graph -- 6.6 Conclusions -- References -- 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 -- References -- Index Intro -- Contents -- Preface -- Contributors -- List of People Involved in the FIRB Project -- 1. The CNN Paradigm for Complexity -- 1.1 Introduction -- 1.2 The 3D-CNN Model -- 1.3 E3: An Universal Emulator for Complex Systems -- 1.4 Emergence of Forms in 3D-CNNs -- 1.4.1 Initial conditions -- 1.4.2 3D waves in homogeneous and unhomogeneous media -- 1.4.3 Chua's circuit -- 1.4.4 Lorenz system -- 1.4.5 Rössler system -- 1.4.6 FitzHugh-Nagumo neuron model -- 1.4.7 Hindmarsh-Rose neuron model -- 1.4.8 Inferior-Olive neuronmodel -- 1.4.9 Izhikevich neuronmodel -- 1.4.10 Neuron model exhibiting homoclinic chaos -- 1.5 Conclusions -- 2. Emergent Phenomena in Neuroscience -- 2.1 Introductory Material: Neurons and Models -- 2.1.1 Models of excitability -- 2.1.2 The Hodgkin-Huxley model -- 2.1.3 The FitzHugh-Nagumo model -- 2.1.4 Class I and class II excitability -- 2.1.5 Other neuronmodels -- 2.2 Electronic Implementation of NeuronModels -- 2.2.1 Implementation of single cell neuron dynamics -- 2.2.2 Implementation of systems with many neurons -- 2.3 Local Activity Theory for Systems of IO Neurons -- 2.3.1 The theory of local activity for one-port and two-port systems -- 2.3.2 The local activity and the edge of chaos regions of the inferior olive neuron -- 2.4 Simulation of IO Systems: Emerging Results -- 2.4.1 The paradigm of local active wave computation for image processing -- 2.4.2 Local active wave computation based paradigm: 3D-shape processing -- 2.5 Networks of HR Neurons -- 2.5.1 The neural model -- 2.5.2 Parameters for dynamical analysis -- 2.5.3 Dynamical effects of topology on synchronization -- 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 -- 3.2.1 Piecewise interaction force -- 3.2.2 Lennard Jones-like interaction force |
| Title | Advanced topics on cellular self-organizing nets and chaotic nonlinear dynamics to model and control complex systems |
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