  Maximum order complexity is an important tool for measuring the nonlinearity
of a pseudorandom sequence. There is a lack of tools for predicting the
strength of a pseudorandom binary sequence in an effective and efficient
manner. To this end, this paper proposes a neural-network-based model for
measuring the strength of a pseudorandom binary sequence. Using the Shrinking
Generator (SG) keystream as pseudorandom binary sequences, then calculating the
Unique Window Size (UWS) as a representation of Maximum order complexity, we
demonstrate that the proposed model provides more accurate and efficient
predictions (measurements) than a classical method for predicting the maximum
order complexity.
