Abstract
The application of the conjugate gradient (CG) method for the identification of bilinear systems is investigated. An algorithm based on the CG method is developed for adaptive-bilinear digital filtering. This algorithm outperforms the least mean square (LMS) and recursive least squares (RLS) methods in terms of speed of convergence. In this algorithm, the optimization is done over blocks of input and output data rather than a single pair data. Only one iteration and coefficient update is done for every sample of data. This, coupled with the fact that the CG method used does not require a line search, makes it very efficient in computation. A preconditioning technique is studied to accelerate the convergence. Some examples are given to illustrate the efficiency of the method. >
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