Abstract
The developments in the early chapters rely on the knowledge of the distribution function F· or its inverse, as well as the threshold C. However, in many 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 methodology of joint identification.
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H.J. Kushner and G. Yin, Stochastic Approximation and Recursive Algorithms and Applications, 2nd ed., Springer-Verlag, New York, 2003.
L.Y. Wang, G. Yin, and J.F. Zhang, Joint identification of plant rational models and noise distribution functions using binary-valued observations, Automatica, 42 (2006), 535–547.
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Wang, L.Y., Yin, G.G., Zhang, JF., Zhao, Y. (2010). Identification of Sensor Thresholds and Noise Distribution Functions. In: System Identification with Quantized Observations. Systems & Control: Foundations & Applications. Birkhäuser Boston. https://doi.org/10.1007/978-0-8176-4956-2_8
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DOI: https://doi.org/10.1007/978-0-8176-4956-2_8
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Online ISBN: 978-0-8176-4956-2
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