The Volterra and Wiener Theories of Nonlinear Systems
The Volterra and Wiener Theories of Nonlinear Systems
A variable step size LMS algorithm
IEEE Transactions on Signal Processing
Nonlinear system identification and prediction using orthogonalfunctions
IEEE Transactions on Signal Processing
Identification of non linear MISO process using RKHS and Volterra models
WSEAS TRANSACTIONS on SYSTEMS
Modelling of a SISO and MIMO non linear communication channel using two modelling techniques
WSEAS Transactions on Circuits and Systems
| Hi-index | 0.00 |
Considered as a prototype of nonlinear system models the second order Volterra filter (FV2) is characterized by an increased complexity in comparison with a linear filter. This complexity is given by the large number of filter coefficients, as well as by the filter operations and it requires an increased computational power in technical applications. The filter based on the multi memory decomposition (MMD) structure represents a good approximation of the FV2 and significantly reduces the number of filter operations. In this paper we propose an efficient implementation of the MMD filter studied in a typical nonlinear identification problem. The simulations show the very good performance of our proposed MMD structure in comparison with the results obtained by using a second order LMS Volterra filter. We have evaluated the performance of our method based on the system response to different input signals.