Identification of hammerstain model using stochastic perturbation simultaneous approximation - Nurriyah, Mohd Noor (2016)
This project study an identification of continuous Hammerstein based on simultaneous Perturbation Stochastic Approximation (SPSA). Furthermore, the Identification is done using MATLAB Simulink to simulate the Hammerstein Model. The structure of non-linear is assumed to be completely unknown. However, the system order assumed to be known For handling it, piecewise-linear function are used as a tool to approximate the unknown non-linear function. The SPSA algorithms was proposed to identify the problem of Hammerstein model. The main benefit of the SPSA-based method is it can be applied to identification of Hammerstein systems even though less restrictive assumptions. The SPSA based method is then used to estimate the parameters in both the linear and non-linear parts based on the given input and output data with the present of delay in time. Besides that, this project analysed the efficient of the SPSA in identify nonlinear system in term of object function and error with different noise variance. A numerical example is given to illustrate that the SPSA based algorithms can give accurate parameter estimate of the Hammerstein models with high probability through detailed simulation.