{"product_id":"systems-theory-in-data-and-optimization-ebook","title":"Systems Theory in Data and Optimization","description":"\u003cp\u003ePart I. Data-Driven and Learning-Based Control.- Chapter 1. PACSBO: Probably Approximately Correct Safe Bayesian Optimization.- Chapter 2. Value of Communication: Data-Driven Topology Optimization for Distributed Linear Cyber-Physical Systems.- Chapter 3. Variance-Informed Model Reference Gaussian Process Regression: Utilizing Variance Information for Control in Nonlinear Systems.- Chapter 4. Data-Driven Dynamic Model and Model Reference Control of Inverter Based Resources.- Chapter 5. Adaptive Tracking MPC for Nonlinear Systems via Online Linear System Identification.- Part II: Machine Learning: Theory and Applications.- Chapter 6. Investigation of the Influence of Training Data and Methods on the Control Performance of MPC Utilizing Gaussian Processes.- Chapter 7. Wiener Chaos in Kernel Regression: Towards Untangling Aleatoric and Epistemic Uncertainty.- Chapter 8. A Universal Reproducing Kernel Hilbert Space for Learning Nonlinear Systems Operators.- Chapter 9. On Robust Reinforcement Learning with Lipschitz-Bounded Policy Networks.- Chapter 10. Solving Partial Differential Equations with Equivariant Extreme Learning Machines.- Chapter 11. Adaptive Robust L2 Loss Function using Fractional Calculus.- Chapter 12. Sparse Reconstruction of Forces, Torques and Velocity Signals for a Swimmer in a Wake.- Chapter 13. Control Theoretic Approach to Fine-Tuning and Transfer Learning.- Part III. Model Predictive Control.- Chapter 14. Accelerating Multi-Objective Model Predictive Control Using High-Order Sensitivity Information.- Chapter 15. On Discount Functions for Economic Model Predictive Control without Terminal Conditions.- Chapter 16. Multi-Parametric Programming with Constraint Telaxation for the Optimal Operation of Micro-Grids Integrating Renewables.- Chapter 17. Multi-Objective Learning Model Predictive Control.- Chapter 18. Terminal Set of Nonlinear Model Predictive Control with Koopman Operators.- Part IV: Optimization.- Chapter 19. Optimal Dynamic Pricing in Energy Markets: A Stackelberg Game Approach.- Chapter 20. Distributed Newton Optimization with ADMM-Based Consensus.- Chapter 21. Inexactness in Bilevel Nonlinear Optimization: A Gradient-free Newton’s Method Approach.\u003c\/p\u003e","brand":"Julian Berberich","offers":[{"title":"Default Title","offer_id":53651262046535,"sku":"9783031831911","price":287.83,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0920\/5455\/2903\/files\/systems-theory-in-data-and-optimization-ebook-cover.webp?v=1775330326","url":"https:\/\/www.cinebuch.de\/products\/systems-theory-in-data-and-optimization-ebook","provider":"CineBuch","version":"1.0","type":"link"}