System Identification with Quantized Observations

Professor Lee Yi Wang

Wayne State University

Department of Electrical and Computer Engineering

Abstract

Binary-valued or quantized sensors are employed in many practical systems. Usually they are more cost effective than regular sensors. In many applications they are the only ones available during real-time operations. Typical examples include switching sensors for exhaust gas oxygen, traffic condition indicators in the ATM (asynchronous transmission mode), neural networks. More important, the new paradigm of sensor networks, networked systems and control, e-health systems for remote monitoring, diagnosis, etc. mandate that signals must be sent over a communication network, and hence must be quantized. In other words, pursuing modeling and control of systems that involve communication channels will need, as a foundation, identification and complexity analysis of system identification with quantized observations. In this talk, recent advances will be presented on system identification with binary or quantized observations. This subject is of importance in understanding modeling capability for systems with limited sensor information, establishing relationships between communication resource limitations and identification complexity, and studying sensor networks. We will start with the fundamental aspects of identification algorithms, strong convergence, convergence rates, and algorithm efficiency (optimality). Understanding from these fundamental issues are then employed to understand such identification problems in various system and environment settings, including different system models (gain, finite impulse response, and rational systems), joint identification of systems and noise distributions, nonlinear systems, impact of communication channels on identification accuracy and speed, selection of quantization thresholds, input design for open-loop and feedback systems, and different scenarios of noises such as actuator noise, input measurement noise, and output measurement noise.

Friday, January 26. 2007

3:30 – 4:30 p.m.

Rm. 1500 EECS