Volterra-type models for nonlinear systems identification

Yapay Ari Koloni̇si̇ Algori̇tmasi İle Opti̇mi̇ze Edi̇len Hammerstein Model Kullanarak Si̇stemleri̇n Ki̇mli̇klendi̇ri̇lmesi̇

“…In this example, considering the structure given in Figure 6, linear system, which is an ARMA [14,62,63], is chosen as in Equation (19). It is identified with four different types of model.…”

“…In literature, SOV structures, mostly only h i and q i,j parameters are taken into consideration, are used in system identification [16,17,19,20]. Because wider structure can be more complex, many researchers study on the block and adaptive applications of Volterra model [17].…”

“…Although very comprehensive, this model involves a large number of parameters, which make it difficult to identify and use. For simplicity, the truncated Volterra series is most often considered in literature [19,20,41].…”

“…Autoregressive, Autoregressive Moving Average (ARMA) models or finite impulse response (FIR) and infinite impulse response (IIR) models are used for linear system identification in literature. Also Volterra, Bilinear and polynomial autoregressive (PAR) models are used for nonlinear system identification [15][16][17][18][19][20][21][22][23][24][25].…”

Implementation of the RN Method on FPGA using Xilinx System Generator for Nonlinear System Regression

“…Comparing with the traditional method, such as Neural Networks [3], [4], Voltera series [5], and the Kernel methods present an attractive alternative. They are well founded in a rigid mathematical structure of Reproducing Kernel Hilbert Spaces (RKHS) [6], [7], it overcomes convex optimization problems.…”