Advance Convergence Characteristic Based on Recycling Buffer Structure in Adaptive Transversal Filter

Provided by: Science & Engineering Research Support soCiety (SERSC)
Topic: Data Management
Format: PDF
The authors extend the use of the least squares method to develop a recursive algorithm for the design of adaptive transversal filters such that, given the least-square estimate of a vector of a filter at iteration, they may compute the updated estimate of this vector at iteration upon the arrival of new data. In this paper, they propose a new tap-weight-updated RLS algorithm for an adaptive transversal filter with data-recycling buffer structure. They prove that the convergence speed of the learning curve of an RLS algorithm with a data-recycling buffer is faster than existing RLS algorithms at mean square error versus iteration number.

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