摘要：AbstractThis paper analyzes conditions for global parametric convergence of a networked recursive identification algorithm. The FIR based algorithm accounts for networked delay and signal quantization. The paper constructs counterexamples to parametric convergence using low order dynamic models and an asymmetric binary quantizer. The associated ODEs of the first order model are analysed with analytical methods and by numerical simulation. In particular, the analysis proves that parametric convergence does not occur in case the input signal distribution is discrete taking a finite number of values, the problem is symmetric, or in case the input signal distribution, dynamic gain and switch point are such that there is no signal energy in an arbitrary small neighborhood of any switch point. Global parametric convergence is also proved for one of the low order cases.
