Nonlinear System Identification Using Hammerstein-Wiener Neural Network and subspace algorithms
Subject Areas : Embedded SystemsMaryam Ashtari Mahini 1 , Mohammad Teshnehlab 2 , Mojtaba Ahmadieh khanehsar 3
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.
Keywords: nonlinear system identification, Neural Network, Hammerstein-Wiener model, state space and subspace identification,
Abstract :
Neural networks are applicable in identification systems from input-output data. In this report, we analyze theHammerstein-Wiener models and identify them. TheHammerstein-Wiener systems are the simplest type of block orientednonlinear systems where the linear dynamic block issandwiched in between two static nonlinear blocks, whichappear in many engineering applications; the aim of nonlinearsystem identification by Hammerstein-Wiener neural networkis finding model order, state matrices and system matrices. Wepropose a robust approach for identifying the nonlinear systemby neural network and subspace algorithms. The subspacealgorithms are mathematically well-established and noniterativeidentification process. The use of subspace algorithmmakes it possible to directly obtain the state space model.Moreover the order of state space model is achieved usingsubspace algorithm. Consequently, by applying the proposedalgorithm, the mean squared error decreases to 0.01 which isless than the results obtained using most approaches in theliterature.
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