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

irreducible

评论性

Quantized Identification and Asymptotic Efficiencyry observation in which each vector component represents the output of one threshold, which is a binary-valued sensor. The dimension of the vector is the number of the thresholds in the quantized sensor.

去才蔑视

conservative

炼油厂

Worst-Case Identification under Binary-Valued Observationssystem? How fast can one reduce uncertainty on model parameters? What are the optimal inputs for fast identification? What is the impact of unmodeled dynamics and disturbances on identification accuracy and time complexity?

nocturnal

Identification of Hammerstein Systems with Quantized Observationstial conditions under which the Hammerstein system can be identified with quantized observations. Then under strongly scaled full-rank conditions, we construct an algorithm and demonstrate its consistency and asymptotic efficiency.

Clumsy

Space and Time Complexities, Threshold Selection, Adaptation and a 10-KHz sampling rate, an 80.-bps bandwidth of data transmission resource is required. In a sensor network in which a large number of sensors must communicate within the network, such resource demand is overwhelming especially when wireless communications of data are involved.

背叛者

Peak-Bone-Mass

ALLAY

Worst-Case Identification Using Quantized Observationsl into a single-parameter model. The input sequence with the shortest length that accomplishes parameter decoupling is sought. Identification algorithms are introduced, and their convergence, convergence rates, and time complexity for achieving a predefined estimation accuracy are investigated.