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A Simulation Study Of Tsypkin's Optimum, Adaptive Quantizer For Unknown Data Processes

Tsypkin's algorithm for optimum, adaptive, recursive quantization of data processes with a priori unknown statistics was simulated. Gaussian and random sinewave data types were used. It is shown in this paper how to choose the initial conditions and gain factors, which Tsypkin left incompletely specified in his English reports, so that the algorithm produces quantizers close to the known optimum quantizers. The cases of 2, 4, and 8 levels are demonstrated. To the author's knowledge, this is the first report on an application of the Tsypkin algorithm in the literature. Further study of Tsypkin's algorithm is recommended.