01222nam0-22004331i-450 99000074731040332120230301112053.088-207-0901-5000074731FED01000074731(Aleph)000074731FED0100007473120131127d1979----km-y0itay50------baitaITy-------001yyProgettazione per ottimizzazioneRolando ScaranoNapoliLiguori1979327 p.21 cm<<La >>società e la scienza6Progettazione architettonica729Scarano,Rolando11377ITUNINARICAUNIMARCBK990000747310403321ARCH B 65611842BFARBCARCH B 13365707FARBCARCH B 16099264BisFARBCARCH B 65511842AFARBC01 GA 5066DINST01 MOD 1153938DINSTB 1513 CAN2255DARPUFONDO ROSSI 4282ROSSI 4403FARBCDARPUFARBCDINSTProgettazione per ottimizzazione322033UNINA03565nam 22006255 450 991068256210332120250327082459.09789819905935981990593110.1007/978-981-99-0593-5(MiAaPQ)EBC7217833(Au-PeEL)EBL7217833(OCoLC)1373985360(DE-He213)978-981-99-0593-5(PPN)269100733(CKB)26291141800041(EXLCZ)992629114180004120230320d2023 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierBayesian Real-Time System Identification From Centralized to Distributed Approach /by Ke Huang, Ka-Veng Yuen1st ed. 2023.Singapore :Springer Nature Singapore :Imprint: Springer,2023.1 online resource (286 pages)Print version: Huang, Ke Bayesian Real-Time System Identification Singapore : Springer Singapore Pte. Limited,c2023 9789819905928 Includes bibliographical references.Chapter 1. Introduction -- Chapter 2. System identification by Kalman filter and extended Kalman filter -- Chapter 3. Outlier detection for real-time system identification -- Chapter 4. Real-time updating of noise parameters for structural identification -- Chapter 5. Bayesian model class selection for real-time system identification -- Chapter 6. Online distributed identification for wireless sensor networks -- Chapter 7. Online distributed identification handling asynchronous data and multiple outlier-corrupted data.This book introduces some recent developments in Bayesian real-time system identification. It contains two different perspectives on data processing for system identification, namely centralized and distributed. A centralized Bayesian identification framework is presented to address challenging problems of real-time parameter estimation, which covers outlier detection, system, and noise parameters tracking. Besides, real-time Bayesian model class selection is introduced to tackle model misspecification problem. On the other hand, a distributed Bayesian identification framework is presented to handle asynchronous data and multiple outlier corrupted data. This book provides sufficient background to follow Bayesian methods for solving real-time system identification problems in civil and other engineering disciplines. The illustrative examples allow the readers to quickly understand the algorithms and associated applications. This book is intended for graduate students and researchersin civil and mechanical engineering. Practitioners can also find useful reference guide for solving engineering problems.DynamicsNonlinear theoriesStatisticsCivil engineeringApplied Dynamical SystemsBayesian InferenceCivil EngineeringDynamics.Nonlinear theories.Statistics.Civil engineering.Applied Dynamical Systems.Bayesian Inference.Civil Engineering.519.542Huang Ke1347168Yuen Ka-VengMiAaPQMiAaPQMiAaPQBOOK9910682562103321Bayesian real-time system identification3419634UNINA