Titlebook: System Identification with Quantized Observations; Le Yi Wang,G. George Yin,Yanlong Zhao Book 2010 Springer Science+Business Media, LLC 20

陈旧

System Settings for the entire book. This chapter provides a problem formulation, shows connections among different system settings, and demonstrates an overall picture of the diverse system identification problems that will be covered in this book. Other than a few common features, technical details are deferred

恶臭

asthma

笼子

Anticoagulant

河流

Identification of Sensor Thresholds and Noise Distribution Functionsany applications, the noise distributions are not known, or only limited information is available. On the other hand, input–output data from the system contain information about the noise distribution. By viewing unknown distributions and system parameters jointly as uncertainties, we develop a meth

anagen

最低点

Worst-Case Identification Using Quantized Observationsn input sequence in (9.5) was used to generate observation equations in which only one parameter appears, reducing the problem to the identification of gain systems. A more general input design method is introduced in this chapter to achieve parameter decoupling that transforms a multiparameter mode

Harrowing

Bombast

Identification of Hammerstein Systems with Quantized Observationsonlinearity that is polynomial and possibly noninvertible, followed by a linear subsystem. The parameters of linear and nonlinear parts are unknown but have known orders.We present input design, identification algorithms, and their essential properties under the assumptions that the distribution fun