Adaptive nonlinear filters equipped with polynomial models of nonlinearity are explained. The polynomial systems considered are those nonlinear systems whose output signals can be related to the input signals through a truncated Volterra series expansion or a recursive nonlinear difference equation. The Volterra series expansion can model a large class of nonlinear systems and is attractive in adaptive filtering applications because the expansion is a linear combination of nonlinear functions of the input signal. The basic ideas behind the development of gradient and recursive least-squares adaptive Volterra filters are first discussed. Adaptive algorithms using system models involving recursive nonlinear difference equations are then treated. Such systems may be able to approximate many nonlinear systems with great parsimony in the use of coefficients. Also discussed are current research trends and new results and problem areas associated with these nonlinear filters. A lattice structure for polynomial models is described. >

Adaptive polynomial filters

https://www.dtc.umn.edu/s/resources/gg-se.pdf

A bibliography on nonlinear system identification

The least squares based iterative algorithms for parameter estimation of a bilinear system with autoregressive noise using the data filtering technique

Polynomial (or Volterra) filters are introduced, and the quadratic filters are presented as the simplest example of such filters. The principle aspects and properties of quadratic filters are derived in the framework of the discrete Volterra expansion. Fixed as well as adaptive filters are considered in one-dimensional and multidimensional environments. Such issues as design and efficient realizations are thoroughly addressed, and standard and advanced adaptation algorithms are presented. Several examples of signal processing applications requiring quadratic filters are discussed. >

Quadratic filters for signal processing

Maximum likelihood methods are based on the probability and statistics theory, and significant for parameter estimation and system modeling. This paper combines the maximum likelihood principle with the data filtering technique for parameter estimation of a class of bilinear systems. The input-output representation of a bilinear system is derived through eliminating the state variables in the model. Then, a filtering based maximum likelihood iterative least squares algorithm is proposed for identifying the parameters of bilinear systems with colored noises by filtering the input-output data with a filter. A least squares based iterative algorithm is given for comparison. The simulation results indicate that the proposed algorithm is effective for identifying bilinear systems. The filtering based maximum likelihood iterative least squares algorithm is more accurate under different noise variance, and has higher computational efficiency.

Maximum Likelihood Least Squares Based Iterative Estimation for a Class of Bilinear Systems Using the Data Filtering Technique

The least-squares method is one of the most efficient and simple identification methods commonly used. Unfortunately, it is very sensitive to large errors (outliers)in the input/output data. In such cases, it may never converge or give erroneous results. In practice, most real systems are nonlinear. Many of these can be suitably represented by bilinear models. In the paper, a robust recursive least-squares method has been proposed for bilinear system identification. It differs from earlier approaches in that it uses modified weights in the criterion for robustness. A theorem proving the convergence of the proposed algorithms included. Results of the simulation demonstrating the robustness of the proposed algorithm are also included.

H. Dai

Papers

Robust recursive least-squares method with modified weights for bilinear system identification