Optimal quantization of signals for system identification

In this paper, we analyse the effect of the quantization of signals used for system identification and show an optimal quantization scheme for minimizing estimation errors under a constraint on the number of subsections of the quantized signals. The optimal quantization scheme has the property that it is coarse near the origin and dense at a distance from it in the definition area of the signals. We also evaluate the estimated parameters and show a trade-off between the quantization error and the noise error under the constraint on the amount of information in the output data.