Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Since most distributed estimation algorithms only try to achieve high estimation precision while ignoring the positive-negative problem of components in the true parameter, estimation using these methods may be physically absurd and uninterpretable. In order to avoid erroneous results,  need to add a nonnegative constraint on the parameter to be estimated. ,  propose a novel distributed diffusion nonnegative LMS algorithm with regularization for estimating some specific parameter. The algorithm keeps the non-negativity of all components in the parameter in the adaptation process. Simulations results illustrate the advantage of our algorithm in the low steady MSD level and high convergence rate.
['Wei Huang', 'Xiaolong Deng', 'Yuzhu Ji', 'Shengyong Chen']
I am writing a research paper on a idea Distributed diffusion nonnegative LMS algorithm over sensor networks
[{"from": "human", "value": "I am writing a research paper on a idea Distributed diffusion nonnegative LMS algorithm over sensor networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Since most distributed estimation algorithms only try to achieve high estimation precision while ignoring the positive-negative problem of components in the true parameter, estimation using these methods may be physically absurd and uninterpretable. In order to avoid erroneous results,  need to add a nonnegative constraint on the parameter to be estimated. ,  propose a novel distributed diffusion nonnegative LMS algorithm with regularization for estimating some specific parameter. The algorithm keeps the non-negativity of all components in the parameter in the adaptation process. Simulations results illustrate the advantage of our algorithm in the low steady MSD level and high convergence rate."}]