Adaptive algorithms for identifying recursive nonlinear systems

The authors present two fast least-squares lattice algorithms for adaptive nonlinear filters equipped with system models involving nonlinear feedback. Such models can approximate a large class of nonlinear systems adequately, and usually with considerable parsimony in the number of coefficients required. For simplicity of presentation, the authors consider the bilinear system model, even though the results are applicable to more general models. The computational complexity of the algorithms is an order of magnitude smaller than that of previously available methods. Results of several experiments that demonstrate the properties of the adaptive bilinear filters are presented. Their performance is compared with that of two other algorithms that are computationally more expensive