Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Sparse adaptive filtering algorithms are utilized to exploit potential sparse structure information as ll as to mitigate noises in many unknown sparse systems. Sparse recursive least square (RLS) algorithms have been attracted intensely attentions due to their low-complexity and easy- implementation. Basically, these algorithms are constructed by standard RLS algorithm and sparse penalty functions (e.g., l_1-norm). Hover, existing sparse RLS algorithms do not exploit the sparsity efficiently. , an improved adaptive filtering algorithm is proposed by incorporating a novel correntropy induced metric (CIM) constraint into RLS, which is termed as RLS- CIM algorithm. Specifically,  adopt a ll-known Gaussian kernel in CIM and further devise a novel variable kernel width to control the sparse penalty in different transient-error scenarios. Numerical simulation results are given to corroborate the proposed algorithm via mean square deviation (MSD).
['Guan Gui', 'Linglong Dai', 'Baoyu Zheng', 'Li Xu', 'Fumiyuki Adachi']
I am writing a research paper on a idea Correntropy Induced Metric Penalized Sparse RLS Algorithm to Improve Adaptive System Identification
[{"from": "human", "value": "I am writing a research paper on a idea Correntropy Induced Metric Penalized Sparse RLS Algorithm to Improve Adaptive System Identification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Sparse adaptive filtering algorithms are utilized to exploit potential sparse structure information as ll as to mitigate noises in many unknown sparse systems. Sparse recursive least square (RLS) algorithms have been attracted intensely attentions due to their low-complexity and easy- implementation. Basically, these algorithms are constructed by standard RLS algorithm and sparse penalty functions (e.g., l_1-norm). Hover, existing sparse RLS algorithms do not exploit the sparsity efficiently. , an improved adaptive filtering algorithm is proposed by incorporating a novel correntropy induced metric (CIM) constraint into RLS, which is termed as RLS- CIM algorithm. Specifically,  adopt a ll-known Gaussian kernel in CIM and further devise a novel variable kernel width to control the sparse penalty in different transient-error scenarios. Numerical simulation results are given to corroborate the proposed algorithm via mean square deviation (MSD)."}]