LEADER 05984nam 2200745Ia 450 001 9910453334503321 005 20200520144314.0 010 $a1-281-96803-X 010 $a9786611968038 010 $a981-281-405-1 035 $a(CKB)1000000000554364 035 $a(EBL)1193349 035 $a(SSID)ssj0000288027 035 $a(PQKBManifestationID)12124699 035 $a(PQKBTitleCode)TC0000288027 035 $a(PQKBWorkID)10373677 035 $a(PQKB)10457689 035 $a(MiAaPQ)EBC1193349 035 $a(WSP)00001987 035 $a(Au-PeEL)EBL1193349 035 $a(CaPaEBR)ebr10688013 035 $a(CaONFJC)MIL196803 035 $a(OCoLC)318879605 035 $a(EXLCZ)991000000000554364 100 $a20081121d2008 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aAdvanced topics on cellular self-organizing nets and chaotic nonlinear dynamics to model and control complex systems$b[electronic resource] /$fedited by Riccardo Caponetto, Luigi Fortuna, Mattia Frasca 210 $aHackensakc, NJ $cWorld Scientific$dc2008 215 $a1 online resource (200 p.) 225 1 $aWorld Scientific series on nonlinear science,$xSeries A,Monographs and treatises ;$vv. 63 300 $aDescription based upon print version of record. 311 $a981-281-404-3 320 $aIncludes bibliographical references and index. 327 $aPreface; Contributors; List of People Involved in the FIRB Project; Contents; 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 Ro?ssler 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 327 $a2. Emergent Phenomena in Neuroscience2.1 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 327 $a2.3.2 The local activity and the edge of chaos regions of the inferior olive neuron2.4 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 327 $a3.2.1 Piecewise interaction force3.2.2 Lennard Jones-like interaction force; 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 327 $a4.3.2 The impact of noise and sampling frequency4.3.3 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 327 $a5.5 Key Elements for the Assessing of Synchronizability 330 $aThis book focuses on the research topics investigated during the three-year research project funded by the Italian Ministero dell'Istruzione, dell'Universita? e della Ricerca (MIUR: Ministry of Education, University and Research) under the FIRB project RBNE01CW3M. With the aim of introducing newer perspectives of the research on complexity, the final results of the project are presented after a general introduction to the subject. The book is intended to provide researchers, PhD students, and people involved in research projects in companies with the basic fundamentals of complex systems and th 410 0$aWorld Scientific series on nonlinear science.$nSeries A,$pMonographs and treatises ;$vv. 63. 606 $aComputational complexity 606 $aNonlinear systems$xMathematical models 606 $aSelf-organizing maps 606 $aSystem theory$xMathematical models 608 $aElectronic books. 615 0$aComputational complexity. 615 0$aNonlinear systems$xMathematical models. 615 0$aSelf-organizing maps. 615 0$aSystem theory$xMathematical models. 676 $a511.3/52 701 $aCaponetto$b R$g(Riccardo),$f1966-$0896479 701 $aFortuna$b L$g(Luigi),$f1953-$0865223 701 $aFrasca$b Mattia$0865222 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910453334503321 996 $aAdvanced topics on cellular self-organizing nets and chaotic nonlinear dynamics to model and control complex systems$92003029 997 $aUNINA