This study investigates the parameter estimation with general quantised inputs and binary-valued output observations for systems with structural uncertainties of the time-dependent bias and the non-linear model mismatch. Combining the empirical-measure-based technique and the least-squares optimisation, an identification algorithm is proposed by use of the output threshold information and the distribution function of the system noise. It is shown that the estimate of the unknown parameter can be sandwiched in between two constructed auxiliary sequences, whose limits are just a lower bound of the limit inferior of the algorithm and an upper bound of the limit superior, respectively. Moreover, probabilistic estimation error bounds are given. Numerical simulations are presented to illustrate the main theoretical results.

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Parameter estimation for systems with structural uncertainties based on quantised inputs and binary-valued output observations

References

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