Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The convergence and the adaptation speed of gradient-based adaptive algorithms are controlled by the chosen value for the convergence factor μ. , the existence of an optimal value for this convergence factor is investigated for two classes of algorithms. A proof is first presented for the general case of the complex adaptive-linear-combiner (ALC). The results are applied to the complex and real LMS algorithms. This is follod by a second proof for algorithms which are linear only in a subset of their adaptive coefficients. These cases are found in IIR applications such as the hybrid-recursive, lattice-recursive, and recursive algorithms using the direct realization IIR. For each case, the optimal value is shown to be generated using instantaneous signal estimates. The resulting adaptive algorithms become self-optimizing in terms of their convergence factor, and dependence on incoming training signal levels is reduced. Moreover, a correction factor is introduced in each case to regulate the adaptation process and accommodate practical applications where additive signals are present with the desired signal.
['Fathy Yassa']
I am writing a research paper on a idea Optimality in the choice of the convergence factor for gradient-based adaptive algorithms
[{"from": "human", "value": "I am writing a research paper on a idea Optimality in the choice of the convergence factor for gradient-based adaptive algorithms"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The convergence and the adaptation speed of gradient-based adaptive algorithms are controlled by the chosen value for the convergence factor \u03bc. , the existence of an optimal value for this convergence factor is investigated for two classes of algorithms. A proof is first presented for the general case of the complex adaptive-linear-combiner (ALC). The results are applied to the complex and real LMS algorithms. This is follod by a second proof for algorithms which are linear only in a subset of their adaptive coefficients. These cases are found in IIR applications such as the hybrid-recursive, lattice-recursive, and recursive algorithms using the direct realization IIR. For each case, the optimal value is shown to be generated using instantaneous signal estimates. The resulting adaptive algorithms become self-optimizing in terms of their convergence factor, and dependence on incoming training signal levels is reduced. Moreover, a correction factor is introduced in each case to regulate the adaptation process and accommodate practical applications where additive signals are present with the desired signal."}]