03545nam a22005175i 4500 978-3-031-52459-2 DE-He213 20251030111736.0 cr nn 008mamaa 240321s2024 sz | s |||| 0|eng d 9783031524592 978-3-031-52459-2 10.1007/978-3-031-52459-2 doi QA402.5-402.6 PBU bicssc MAT042000 bisacsh PBU thema Toscano, Rosario. author. aut http://id.loc.gov/vocabulary/relators/aut 73665 Solving Optimization Problems with the Heuristic Kalman Algorithm [electronic resource] : New Stochastic Methods / by Rosario Toscano. 1st ed. 2024. Cham : Springer International Publishing : Imprint: Springer, 2024. XX, 286 p. 1 illus. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Springer Optimization and Its Applications, 1931-6836 ; 212 1 Introduction -- 2 Stochastic Optimization Methods -- 3 Heuristic Kalman Algorithm -- 4 Some Notions on System Modeling -- 5 Robust Control of Uncertain Parametric Systems -- 6 Preventive Maintenance -- 7 Machine Learning -- 8 Conclusion -- A Signal and System Norms -- B Convergence Properties of the HKA and Program Code -- References -- Index. This text focuses on simple and easy-to-use design strategies for solving complex engineering problems that arise in several fields of engineering design, namely non-convex optimization problems. The main optimization tool used in this book to tackle the problem of nonconvexity is the Heuristic Kalman Algorithm (HKA). The main characteristic of HKA is the use of a stochastic search mechanism to solve a given optimization problem. From a computational point of view, the use of a stochastic search procedure appears essential for dealing with non-convex problems. The topics discussed in this monograph include basic definitions and concepts from the classical optimization theory, the notion of the acceptable solution, machine learning, the concept of preventive maintenance, and more. The Heuristic Kalman Algorithm discussed in this book applies to many fields such as robust structured control, electrical engineering, mechanical engineering, machine learning, reliability, and preference models. This large coverage of practical optimization problems makes this text very useful to those working on and researching systems design. The intended audience includes industrial engineers, postgraduates, and final-year undergraduates in various fields of systems design. . Mathematical optimization. 65522 Algorithms. 58677 Optimization. 65528 Algorithms. 58677 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031524585 Printed edition: 9783031524608 Printed edition: 9783031524615 Springer Optimization and Its Applications, 1931-6836 ; 212 73211 https://doi.org/10.1007/978-3-031-52459-2 ZDB-2-SMA ZDB-2-SXMS 58545 58545