Abstract
This paper compares the performance of three different types of periodic binary sequences in the identification of the Best Linear Approximation of a nonlinear system. The signal types considered are discrete interval random binary sequences (DIRBS), maximum length binary sequences (MLBS) and inverse-repeat binary sequences (IRBS). It is found that MLBS's offer advantages when experiment time limitation prohibits a large amount of averaging. IRBS's have the advantage that even order nonlinear contributions do not affect the quality of the estimate, but the disadvantage of either a longer experiment time or a lower frequency resolution.
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