Applications of nonlinear filters with the linear-in-the-parameter structure
Eng Siong Chng · 1995
The subject of this thesis is the application of nonlinear filters, with the linear-in-the-parameter structure, to time series prediction and channel equalisation problems. In particular, the Volterra and the radial basis function (RBF) expansion techniques are considered to implement the nonlinear filter structures. These approaches, however, will generate filters with very large numbers of parameters. As large filter models require significant implementation complexity, they are undesirable for practical implementations. To reduce the size of the filter, the orthogonal least squares (OLS) algorithm is considered to perform model selection. Simulations were conducted to study the effectiveness of subset models found using this algorithm, and the results indicate that this selection technique is adequate for many practical applications. The other aspect of the OLS algorithm studied is its implementation requirements. Although the OLS algorithm is very efficient, the required computatio...