variable gain design stochastic di shen dong (13 risultati)

Condizione: New. SUPER FAST SHIPPING.

Condizione: New. In.

Hardcover. Condizione: new. Hardcover. This book investigates the critical gain design in stochastic iterative learning control (SILC), including four specific gain design strategies: decreasing gain design, adaptive gain design, event-triggering gain design, and optimal gain design. The key concept for the gain design is to balance multiple performance indices such as high tracking precision, effective noise reduction, and fast convergence speed. These gain design techniques can be applied to various control algorithms for stochastic systems to realize a high tracking performance. This book provides a series of design and analysis techniques for the establishment of a systematic framework of gain design in SILC. The book is intended for scholars and graduate students who are interested in stochastic control, recursive algorithms design, and iterative learning control. This book investigates the critical gain design in stochastic iterative learning control (SILC), including four specific gain design strategies: decreasing gain design, adaptive gain design, event-triggering gain design, and optimal gain design. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Condizione: New.

Hardcover. Condizione: new. Hardcover. This book investigates the critical gain design in stochastic iterative learning control (SILC), including four specific gain design strategies: decreasing gain design, adaptive gain design, event-triggering gain design, and optimal gain design. The key concept for the gain design is to balance multiple performance indices such as high tracking precision, effective noise reduction, and fast convergence speed. These gain design techniques can be applied to various control algorithms for stochastic systems to realize a high tracking performance. This book provides a series of design and analysis techniques for the establishment of a systematic framework of gain design in SILC. The book is intended for scholars and graduate students who are interested in stochastic control, recursive algorithms design, and iterative learning control. This book investigates the critical gain design in stochastic iterative learning control (SILC), including four specific gain design strategies: decreasing gain design, adaptive gain design, event-triggering gain design, and optimal gain design. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condizione: New. 2024th edition NO-PA16APR2015-KAP.

Hardcover. Condizione: new. Hardcover. This book investigates the critical gain design in stochastic iterative learning control (SILC), including four specific gain design strategies: decreasing gain design, adaptive gain design, event-triggering gain design, and optimal gain design. The key concept for the gain design is to balance multiple performance indices such as high tracking precision, effective noise reduction, and fast convergence speed. These gain design techniques can be applied to various control algorithms for stochastic systems to realize a high tracking performance. This book provides a series of design and analysis techniques for the establishment of a systematic framework of gain design in SILC. The book is intended for scholars and graduate students who are interested in stochastic control, recursive algorithms design, and iterative learning control. This book investigates the critical gain design in stochastic iterative learning control (SILC), including four specific gain design strategies: decreasing gain design, adaptive gain design, event-triggering gain design, and optimal gain design. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Buch. Condizione: Neu. Neuware -This book investigates the critical gain design in stochastic iterative learning control (SILC), including four specific gain design strategies: decreasing gain design, adaptive gain design, event-triggering gain design, and optimal gain design. The key concept for the gain design is to balance multiple performance indices such as high tracking precision, effective noise reduction, and fast convergence speed. These gain design techniques can be applied to various control algorithms for stochastic systems to realize a high tracking performance. This book provides a series of design and analysis techniques for the establishment of a systematic framework of gain design in SILC. The book is intended for scholars and graduate students who are interested in stochastic control, recursive algorithms design, and iterative learning control.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 364 pp. Englisch.

Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book investigates the critical gain design in stochastic iterative learning control (SILC), including four specific gain design strategies: decreasing gain design, adaptive gain design, event-triggering gain design, and optimal gain design. The key concept for the gain design is to balance multiple performance indices such as high tracking precision, effective noise reduction, and fast convergence speed. These gain design techniques can be applied to various control algorithms for stochastic systems to realize a high tracking performance. This book provides a series of design and analysis techniques for the establishment of a systematic framework of gain design in SILC. The book is intended for scholars and graduate students who are interested in stochastic control, recursive algorithms design, and iterative learning control.

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book investigates the critical gain design in stochastic iterative learning control (SILC), including four specific gain design strategies: decreasing gain design, adaptive gain design, event-triggering gain design, and optimal gain design. The key concept for the gain design is to balance multiple performance indices such as high tracking precision, effective noise reduction, and fast convergence speed. These gain design techniques can be applied to various control algorithms for stochastic systems to realize a high tracking performance. This book provides a series of design and analysis techniques for the establishment of a systematic framework of gain design in SILC. The book is intended for scholars and graduate students who are interested in stochastic control, recursive algorithms design, and iterative learning control. 348 pp. Englisch.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

Condizione: New. Print on Demand.

Condizione: New. PRINT ON DEMAND.