Nonlinear adaptive filtering in nonstationary environments

The relationship of the F-projections adaptive algorithm to the LMS (least mean square), RLS (recursive least squares), and Kalman algorithms is investigated. A recursive form of nonlinear least squares is developed, and the conditions under which the F-projections algorithm becomes equivalent to it are established. A radial basis function neural network is used as a nonlinear model in analyzing time series under nonstationary environments. The performances of the F-projections and the extended Kalman algorithms for this nonlinear model in predicting a chaotic series and in tracking a time-varying system are compared