03187nam a22004335i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001800153072001600171072002300187072001500210100007900225245014200304250001800446264007500464300004200539336002600581337002600607338003600633347002400669490006600693505035200759520128101111650003102392650001602423650001802439650001602457710003402473773002602507776003602533776003602569776003602605830006602641856004602707978-3-031-52459-2DE-He21320251030111736.0cr nn 008mamaa240321s2024    sz |    s    |||| 0|eng d  a97830315245927 a10.1007/978-3-031-52459-22doi 4aQA402.5-402.6 7aPBU2bicssc 7aMAT0420002bisacsh 7aPBU2thema1 aToscano, Rosario.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aSolving Optimization Problems with the Heuristic Kalman Algorithmh[electronic resource] :bNew Stochastic Methods /cby Rosario Toscano.  a1st ed. 2024. 1aCham :bSpringer International Publishing :bImprint: Springer,c2024.  aXX, 286 p. 1 illus.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aSpringer Optimization and Its Applications,x1931-6836 ;v2120 a1 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.  aThis 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. . 0aMathematical optimization. 0aAlgorithms.14aOptimization.24aAlgorithms.2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303152458508iPrinted edition:z978303152460808iPrinted edition:z9783031524615 0aSpringer Optimization and Its Applications,x1931-6836 ;v21240uhttps://doi.org/10.1007/978-3-031-52459-2