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Applied Physics, System Science and Computers III : Proceedings of the 3rd International Conference on Applied Physics, System Science and Computers (APSAC2018), September 26-28, 2018, Dubrovnik, Croatia / / edited by Klimis Ntalianis, George Vachtsevanos, Pierre Borne, Anca Croitoru
| Applied Physics, System Science and Computers III : Proceedings of the 3rd International Conference on Applied Physics, System Science and Computers (APSAC2018), September 26-28, 2018, Dubrovnik, Croatia / / edited by Klimis Ntalianis, George Vachtsevanos, Pierre Borne, Anca Croitoru |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XI, 356 p. 142 illus., 99 illus. in color.) |
| Disciplina |
571.4
530 |
| Collana | Lecture Notes in Electrical Engineering |
| Soggetto topico |
Biophysics
Biological physics Electrical engineering Mathematical models Manufactures Biological and Medical Physics, Biophysics Electrical Engineering Mathematical Modeling and Industrial Mathematics Manufacturing, Machines, Tools, Processes |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Doppler Delay in Navigation Signals Received by GNSS Receivers -- Experimental Setup for H2/O2 Small Thruster Evaluation -- Tracking the Evolution of Functional Connectivity Patterns between Pancreatic Beta Cells with Multilayer Network Formalism -- Silver Thin Film and Water Dielectric SPR Sensor Experimental and Simulation Characteristics -- On the Solution of the Fredholm Equation with the use of Quadratic Integro-Differential Splines. |
| Record Nr. | UNINA-9910337876803321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Multiple models approach in automation [[electronic resource] ] : takagi-sugeno fuzzy systems / / Mohammed Chadli, Pierre Borne ; series editor, Bernard Dubuisson
| Multiple models approach in automation [[electronic resource] ] : takagi-sugeno fuzzy systems / / Mohammed Chadli, Pierre Borne ; series editor, Bernard Dubuisson |
| Autore | Chadli Mohammed |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | London, : ISTE |
| Descrizione fisica | 1 online resource (204 p.) |
| Disciplina |
004.1
629.80151 |
| Altri autori (Persone) |
BornePierre
DubuissonBernard |
| Collana | Automation - control and industrial engineering series |
| Soggetto topico |
Automation
Fuzzy systems |
| ISBN |
1-118-57732-9
1-299-18659-9 1-118-57729-9 1-118-57722-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Title Page; Contents; Notations; Introduction; Chapter 1. Multiple Model Representation; 1.1. Introduction; 1.2. Techniques for obtaining multiple models; 1.2.1. Construction of multiple models by identification; 1.2.2. Multiple model construction by linearization; 1.2.3. Multiple model construction by mathematical transformation; 1.2.4. Multiple model representation using the neural approach; 1.3. Analysis and synthesis tools; 1.3.1. Lyapunov approach; 1.3.2. Numeric tools: linear matrix inequalities; 1.3.3. Multiple model control techniques
Chapter 2. Stability of Continuous Multiple Models2.1. Introduction; 2.2. Stability analysis; 2.2.1. Exponential stability; 2.3. Relaxed stability; 2.4. Example; 2.5. Robust stability; 2.5.1. Norm-bounded uncertainties; 2.5.2. Structured parametric uncertainties; 2.5.3. Analysis of nominal stability; 2.5.4. Analysis of robust stability; 2.6. Conclusion; Chapter 3. Multiple Model State Estimation; 3.1. Introduction; 3.2. Synthesis of multiple observers; 3.2.1. Linearization; 3.2.2. Pole placement; 3.2.3. Application: asynchronous machine; 3.2.4. Synthesis of multiple observers 3.3. Multiple observer for an uncertain multiple model3.4. Synthesis of unknown input observers; 3.4.1. Unknown inputs affecting system state; 3.4.2. Unknown inputs affecting system state and output; 3.4.3. Estimation of unknown inputs; 3.5. Synthesis of unknown input observers: another approach; 3.5.1. Principle; 3.5.2. Multiple observers subject to unknown inputs and uncertainties; 3.6. Conclusion; Chapter 4. Stabilization of Multiple Models; 4.1. Introduction; 4.2. Full state feedback control; 4.2.1. Linearization; 4.2.2. Specific case; 4.2.3. α-stability: decay rate 4.3. Observer-based controller4.3.1. Unmeasurable decision variables; 4.4. Static output feedback control; 4.4.1. Pole placement; 4.5. Conclusion; Chapter 5. Robust Stabilization of Multiple Models; 5.1. Introduction; 5.2. State feedback control.; 5.2.1. Norm-bounded uncertainties; 5.2.2. Interval uncertainties; 5.3. Output feedback control; 5.3.1. Norm-bounded uncertainties; 5.3.2. Interval uncertainties; 5.4. Observer-based control; 5.5. Conclusion; Conclusion; APPENDICES; Appendix 1: LMI Regions; A1.1. Definition of an LMI region; A1.2. Interesting LMI region examples A1.2.1. Open left half-planeA1.2.2. α-stability; A1.2.3. Vertical band; A1.2.4. Horizontal band; A1.2.5. Disk of radius R, centered at (q,0); A1.2.6. Conical sector.; Appendix 2: Properties of M-Matrices; Appendix 3: Stability and Comparison Systems; A3.1. Vector norms and overvaluing systems; A3.1.1. Definition of a vector norm; A3.1.2. Definition of a system overvalued from a continuous process; A3.1.3. Application; A3.2. Vector norms and the principle of comparison; A3.3. Application to stability analysis; Bibliography; Index |
| Record Nr. | UNINA-9910141495503321 |
Chadli Mohammed
|
||
| London, : ISTE | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Multiple models approach in automation : takagi-sugeno fuzzy systems / / Mohammed Chadli, Pierre Borne ; series editor, Bernard Dubuisson
| Multiple models approach in automation : takagi-sugeno fuzzy systems / / Mohammed Chadli, Pierre Borne ; series editor, Bernard Dubuisson |
| Autore | Chadli Mohammed |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | London, : ISTE |
| Descrizione fisica | 1 online resource (204 p.) |
| Disciplina |
004.1
629.80151 |
| Altri autori (Persone) |
BornePierre
DubuissonBernard |
| Collana | Automation - control and industrial engineering series |
| Soggetto topico |
Automation
Fuzzy systems |
| ISBN |
1-118-57732-9
1-299-18659-9 1-118-57729-9 1-118-57722-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Title Page; Contents; Notations; Introduction; Chapter 1. Multiple Model Representation; 1.1. Introduction; 1.2. Techniques for obtaining multiple models; 1.2.1. Construction of multiple models by identification; 1.2.2. Multiple model construction by linearization; 1.2.3. Multiple model construction by mathematical transformation; 1.2.4. Multiple model representation using the neural approach; 1.3. Analysis and synthesis tools; 1.3.1. Lyapunov approach; 1.3.2. Numeric tools: linear matrix inequalities; 1.3.3. Multiple model control techniques
Chapter 2. Stability of Continuous Multiple Models2.1. Introduction; 2.2. Stability analysis; 2.2.1. Exponential stability; 2.3. Relaxed stability; 2.4. Example; 2.5. Robust stability; 2.5.1. Norm-bounded uncertainties; 2.5.2. Structured parametric uncertainties; 2.5.3. Analysis of nominal stability; 2.5.4. Analysis of robust stability; 2.6. Conclusion; Chapter 3. Multiple Model State Estimation; 3.1. Introduction; 3.2. Synthesis of multiple observers; 3.2.1. Linearization; 3.2.2. Pole placement; 3.2.3. Application: asynchronous machine; 3.2.4. Synthesis of multiple observers 3.3. Multiple observer for an uncertain multiple model3.4. Synthesis of unknown input observers; 3.4.1. Unknown inputs affecting system state; 3.4.2. Unknown inputs affecting system state and output; 3.4.3. Estimation of unknown inputs; 3.5. Synthesis of unknown input observers: another approach; 3.5.1. Principle; 3.5.2. Multiple observers subject to unknown inputs and uncertainties; 3.6. Conclusion; Chapter 4. Stabilization of Multiple Models; 4.1. Introduction; 4.2. Full state feedback control; 4.2.1. Linearization; 4.2.2. Specific case; 4.2.3. α-stability: decay rate 4.3. Observer-based controller4.3.1. Unmeasurable decision variables; 4.4. Static output feedback control; 4.4.1. Pole placement; 4.5. Conclusion; Chapter 5. Robust Stabilization of Multiple Models; 5.1. Introduction; 5.2. State feedback control.; 5.2.1. Norm-bounded uncertainties; 5.2.2. Interval uncertainties; 5.3. Output feedback control; 5.3.1. Norm-bounded uncertainties; 5.3.2. Interval uncertainties; 5.4. Observer-based control; 5.5. Conclusion; Conclusion; APPENDICES; Appendix 1: LMI Regions; A1.1. Definition of an LMI region; A1.2. Interesting LMI region examples A1.2.1. Open left half-planeA1.2.2. α-stability; A1.2.3. Vertical band; A1.2.4. Horizontal band; A1.2.5. Disk of radius R, centered at (q,0); A1.2.6. Conical sector.; Appendix 2: Properties of M-Matrices; Appendix 3: Stability and Comparison Systems; A3.1. Vector norms and overvaluing systems; A3.1.1. Definition of a vector norm; A3.1.2. Definition of a system overvalued from a continuous process; A3.1.3. Application; A3.2. Vector norms and the principle of comparison; A3.3. Application to stability analysis; Bibliography; Index |
| Altri titoli varianti | Takagi-Sugeno fuzzy systems |
| Record Nr. | UNINA-9910825968103321 |
|
Chadli Mohammed
|
||
| London, : ISTE | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Optimization in engineering sciences [[electronic resource] ] : exact methods / / Pierre Borne ... [et al.]
| Optimization in engineering sciences [[electronic resource] ] : exact methods / / Pierre Borne ... [et al.] |
| Autore | Borne Pierre |
| Pubbl/distr/stampa | Hoboken, N.J., : ISTE Ltd/John Wiley and Sons Inc., 2013 |
| Descrizione fisica | 1 online resource (328 p.) |
| Disciplina |
519.92
629.89 |
| Altri autori (Persone) | BornePierre |
| Collana | ISTE |
| Soggetto topico |
Engineering mathematics
Mathematical optimization Program transformation (Computer programming) Algorithms Systems engineering |
| ISBN |
1-118-57789-2
1-299-14153-6 1-118-57775-2 1-118-57784-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Title Page; Contents; Foreword; Preface; List of Acronyms; Chapter 1. Linear Programming; 1.1. Objective of linear programming; 1.2. Stating the problem; 1.3. Lagrange method; 1.4. Simplex algorithm; 1.4.1. Principle; 1.4.2. Simplicial form formulation; 1.4.3. Transition from one simplicial form to another; 1.4.4. Summary of the simplex algorithm; 1.5. Implementation example; 1.6. Linear programming applied to the optimization of resource allocation; 1.6.1. Areas of application; 1.6.2. Resource allocation for advertising; 1.6.3. Optimization of a cut of paper rolls
1.6.4. Structure of linear program of an optimal control problemChapter 2. Nonlinear Programming; 2.1. Problem formulation; 2.2. Karush-Kuhn-Tucker conditions; 2.3. General search algorithm; 2.3.1. Main steps; 2.3.2. Computing the search direction; 2.3.3. Computation of advancement step; 2.4. Monovariable methods; 2.4.1. Coggin's method (of polynomial interpolation); 2.4.2. Golden section method; 2.5. Multivariable methods; 2.5.1. Direct search methods; 2.5.2. Gradient methods; Chapter 3. Dynamic Programming; 3.1. Principle of dynamic programming; 3.1.1. Stating the problem 3.1.2. Decision problem3.2. Recurrence equation of optimality; 3.3. Particular cases; 3.3.1. Infinite horizon stationary problems; 3.3.2. Variable horizon problem; 3.3.3. Random horizon problem; 3.3.4. Taking into account sum-like constraints; 3.3.5. Random evolution law; 3.3.6. Initialization when the final state is imposed; 3.3.7. The case when the necessary information is not always available; 3.4. Examples; 3.4.1. Route optimization; 3.4.2. The smuggler problem; Chapter 4. Hopfield Networks; 4.1. Structure; 4.2. Continuous dynamic Hopfield networks; 4.2.1. General problem 4.2.2. Application to the traveling salesman problem4.3. Optimization by Hopfield networks, based on simulated annealing; 4.3.1. Deterministic method; 4.3.2. Stochastic method; Chapter 5. Optimization in System Identification; 5.1. The optimal identification principle; 5.2. Formulation of optimal identification problems; 5.2.1. General problem; 5.2.2. Formulation based on optimization theory; 5.2.3. Formulation based on estimation theory (statistics); 5.3. Usual identification models; 5.3.1. General model; 5.3.2. Rational input/output (RIO) models 5.3.3. Class of autoregressive models (ARMAX)5.3.4. Class of state space representation models; 5.4. Basic least squares method; 5.4.1. LSM type solution; 5.4.2. Geometric interpretation of the LSM solution; 5.4.3. Consistency of the LSM type solution; 5.4.4. Example of application of the LSM for an ARX model; 5.5. Modified least squares methods; 5.5.1. Recovering lost consistency; 5.5.2. Extended LSM; 5.5.3. Instrumental variables method; 5.6. Minimum prediction error method; 5.6.1. Basic principle and algorithm; 5.6.2. Implementation of the MPEM for ARMAX models 5.6.3. Convergence and consistency of MPEM type estimations |
| Record Nr. | UNINA-9910141493603321 |
|
Borne Pierre
|
||
| Hoboken, N.J., : ISTE Ltd/John Wiley and Sons Inc., 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Optimization in engineering sciences : exact methods / / Pierre Borne ... [et al.]
| Optimization in engineering sciences : exact methods / / Pierre Borne ... [et al.] |
| Autore | Borne Pierre |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Hoboken, N.J., : ISTE Ltd/John Wiley and Sons Inc., 2013 |
| Descrizione fisica | 1 online resource (328 p.) |
| Disciplina |
519.92
629.89 |
| Altri autori (Persone) | BornePierre |
| Collana | ISTE |
| Soggetto topico |
Engineering mathematics
Mathematical optimization Program transformation (Computer programming) Algorithms Systems engineering |
| ISBN |
1-118-57789-2
1-299-14153-6 1-118-57775-2 1-118-57784-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Title Page; Contents; Foreword; Preface; List of Acronyms; Chapter 1. Linear Programming; 1.1. Objective of linear programming; 1.2. Stating the problem; 1.3. Lagrange method; 1.4. Simplex algorithm; 1.4.1. Principle; 1.4.2. Simplicial form formulation; 1.4.3. Transition from one simplicial form to another; 1.4.4. Summary of the simplex algorithm; 1.5. Implementation example; 1.6. Linear programming applied to the optimization of resource allocation; 1.6.1. Areas of application; 1.6.2. Resource allocation for advertising; 1.6.3. Optimization of a cut of paper rolls
1.6.4. Structure of linear program of an optimal control problemChapter 2. Nonlinear Programming; 2.1. Problem formulation; 2.2. Karush-Kuhn-Tucker conditions; 2.3. General search algorithm; 2.3.1. Main steps; 2.3.2. Computing the search direction; 2.3.3. Computation of advancement step; 2.4. Monovariable methods; 2.4.1. Coggin's method (of polynomial interpolation); 2.4.2. Golden section method; 2.5. Multivariable methods; 2.5.1. Direct search methods; 2.5.2. Gradient methods; Chapter 3. Dynamic Programming; 3.1. Principle of dynamic programming; 3.1.1. Stating the problem 3.1.2. Decision problem3.2. Recurrence equation of optimality; 3.3. Particular cases; 3.3.1. Infinite horizon stationary problems; 3.3.2. Variable horizon problem; 3.3.3. Random horizon problem; 3.3.4. Taking into account sum-like constraints; 3.3.5. Random evolution law; 3.3.6. Initialization when the final state is imposed; 3.3.7. The case when the necessary information is not always available; 3.4. Examples; 3.4.1. Route optimization; 3.4.2. The smuggler problem; Chapter 4. Hopfield Networks; 4.1. Structure; 4.2. Continuous dynamic Hopfield networks; 4.2.1. General problem 4.2.2. Application to the traveling salesman problem4.3. Optimization by Hopfield networks, based on simulated annealing; 4.3.1. Deterministic method; 4.3.2. Stochastic method; Chapter 5. Optimization in System Identification; 5.1. The optimal identification principle; 5.2. Formulation of optimal identification problems; 5.2.1. General problem; 5.2.2. Formulation based on optimization theory; 5.2.3. Formulation based on estimation theory (statistics); 5.3. Usual identification models; 5.3.1. General model; 5.3.2. Rational input/output (RIO) models 5.3.3. Class of autoregressive models (ARMAX)5.3.4. Class of state space representation models; 5.4. Basic least squares method; 5.4.1. LSM type solution; 5.4.2. Geometric interpretation of the LSM solution; 5.4.3. Consistency of the LSM type solution; 5.4.4. Example of application of the LSM for an ARX model; 5.5. Modified least squares methods; 5.5.1. Recovering lost consistency; 5.5.2. Extended LSM; 5.5.3. Instrumental variables method; 5.6. Minimum prediction error method; 5.6.1. Basic principle and algorithm; 5.6.2. Implementation of the MPEM for ARMAX models 5.6.3. Convergence and consistency of MPEM type estimations |
| Record Nr. | UNINA-9910806124003321 |
|
Borne Pierre
|
||
| Hoboken, N.J., : ISTE Ltd/John Wiley and Sons Inc., 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||