Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In linear filtering, the set-membership normalized least mean squares (SM-NLMS) algorithm has been shown to exhibit desirable features of selective update and optimized variable step size. , a kernel approach to the SM-NLMS algorithm is presented that makes it feasible to address nonlinear problems. An online greedy approximation technique to achieve sparsity is discussed. Simulation results are presented for two practical problems: equalization of nonlinear inter-symbol interference (ISI) channels and predistortion of nonlinear high por amplifiers (HPA).
['Amaresh V. Malipatil', 'Yih-Fang Huang', 'Srinivas Andra', 'Kristin P. Bennett']
I am writing a research paper on a idea Kernelized set-membership approach to nonlinear adaptive filtering
[{"from": "human", "value": "I am writing a research paper on a idea Kernelized set-membership approach to nonlinear adaptive filtering"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In linear filtering, the set-membership normalized least mean squares (SM-NLMS) algorithm has been shown to exhibit desirable features of selective update and optimized variable step size. , a kernel approach to the SM-NLMS algorithm is presented that makes it feasible to address nonlinear problems. An online greedy approximation technique to achieve sparsity is discussed. Simulation results are presented for two practical problems: equalization of nonlinear inter-symbol interference (ISI) channels and predistortion of nonlinear high por amplifiers (HPA)."}]