Nonlinear System Identification Using Hammerstein-Wiener Neural Network and subspace algorithms

Neural networks are applicable in identification 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 literature.