14th IFAC Symposium on System Identification, SYSID 2006

SYSID-2006 Paper Abstract


Paper WeA4.1

Wang, Le Yi (Wayne State Univ.), Yin, George (Wayne State Univ.), Zhang, Ji-Feng (Chinese Acad. of Sciences)

Rational Model Identification with Unknown Noise Distribution Using Binary Data

Scheduled for presentation during the Regular Session "Identification for Control" (WeA4), Wednesday, March 29, 2006, 10:30−10:50, Cummings Room

14th IFAC Symposium on System Identification, March 29 - 31, 2006, Newcastle, Australia

This information is tentative and subject to change. Compiled on July 17, 2018

Keywords Identification for Control, Recursive Identification


System identification of plants with binary-valued output observations is of importance in understanding modeling capability and limitations for systems with limited sensor information, establishing relationships between communication resource limitations and identification complexity, and studying sensor networks. This paper resolves two issues arising in such system identification problems. First, regression structures for identifying a rational model contain non-smooth nonlinearity, leading to a difficult nonlinear filtering problem. By introducing a two-step identification procedure that employs periodic signals, empirical measures, and identifiability features, rational models can be identified without resorting to complicated nonlinear searching algorithms. Second, by formulating a joint identification problem, we are able to accommodate scenarios in which noise distribution functions are unknown. Convergence of parameter estimates and recursive algorithms are derived.