  Blind System Identification (BSI) is used to extract a system model whenever
input data is not attainable. Therefore, the input data and system model should
be estimated simultaneously. Because of nonlinearities in a large number of
systems, BSI problem is usually challenging to solve. In this paper, an
innovative solution is proposed to deal with the BSI problem in nonlinear
systems using the properties of the Linear Parameter-Varying (LPV) systems and
Hidden Markov Models (HMM). More specifically, assuming scheduling variable is
not measurable, the dynamic of the LPV system is approximated. To solve the BSI
problem in this context, LPV structure is modeled as an HMM network and a
modified Quasi-Static combination of Viterbi and Baum-Welch algorithms (QSVBW)
is proposed to estimate the nonlinear mappings and scheduling variable signal.
The applicability and the performance of the suggested QSVBW algorithm has been
justified by numerical studies.
