Document Type: Original Research Paper


1 Dept of Computer Engineering. Science and Research Branch, Islamic Azad University, Tehran, Iran.

2 Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

3 Department of Control Engineering, Semnan University, Semnan, Iran.


Neural networks are applicable in identification systems
from input-output data. In this report, we analyze the
Hammerstein-Wiener models and identify them. The
Hammerstein-Wiener systems are the simplest type of block oriented
nonlinear systems where the linear dynamic block is
sandwiched in between two static nonlinear blocks, which
appear in many engineering applications; the aim of nonlinear
system identification by Hammerstein-Wiener neural network
is finding model order, state matrices and system matrices. We
propose a robust approach for identifying the nonlinear system
by neural network and subspace algorithms. The subspace
algorithms are mathematically well-established and noniterative
identification process. The use of subspace algorithm
makes it possible to directly obtain the state space model.
Moreover the order of state space model is achieved using
subspace algorithm. Consequently, by applying the proposed
algorithm, the mean squared error decreases to 0.01 which is
less than the results obtained using most approaches in the


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