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Solving optimization problems with the heuristic Kalman algorithm = new stochastic methods /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Solving optimization problems with the heuristic Kalman algorithm/ by Rosario Toscano.
其他題名:	new stochastic methods /
作者:	Toscano, Rosario.
出版者:	Cham :Springer International Publishing : : 2024.,
面頁冊數:	xx, 286 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Stochastic processes. -
電子資源:	https://doi.org/10.1007/978-3-031-52459-2
ISBN:	9783031524592

Solving optimization problems with the heuristic Kalman algorithm = new stochastic methods /
Toscano, Rosario.

Solving optimization problems with the heuristic Kalman algorithmnew stochastic methods /[electronic resource] :by Rosario Toscano. - Cham :Springer International Publishing :2024. - xx, 286 p. :ill., digital ;24 cm. - Springer optimization and its applications,v. 2121931-6836 ;. - Springer optimization and its applications ;v. 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.

ISBN: 9783031524592

Standard No.: 10.1007/978-3-031-52459-2doiSubjects--Topical Terms:

520663
Stochastic processes.

LC Class. No.: QA274 / .T67 2024

Dewey Class. No.: 519.23

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520 $a 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.
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