Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Sparsity phenomena in learning processes have been extensively studied, since their detection allows to derive suited regularized optimization algorithms capable of improving the overall learning performance. ,  investigate the sparsity behavior that may occur in nonlinear adaptive filtering problems and how to leverage it and develop enhanced algorithms. In particular,  focus on a particular class of linear-in-the-parameters nonlinear adaptive filters, whose nonlinear transformation is based on a functional link expansion. The analysis of the sparsity in functional links leads us to derive a family of adaptive combined filtering architectures that is capable of exploiting any sparseness degree in the nonlinear filtering.  propose two different filtering schemes based on a new block-based combined approach, ll suited for sparse adaptive algorithms. Moreover, a hierarchical architecture is also proposed that generalizes the different combined schemes and does not need any a priori information about the nature of the nonlinearity to be modeled. Experimental results prove the effectiveness of the proposed combined architectures in exploiting any sparseness degree and improving the modeling performance in nonlinear system identification problems.
['Danilo Comminiello', 'Michele Scarpiniti', 'Luis Antonio Azpicueta-Ruiz', 'Jerónimo Arenas-García', 'Aurelio Uncini']
I am writing a research paper on a idea Combined nonlinear filtering architectures involving sparse functional link adaptive filters
[{"from": "human", "value": "I am writing a research paper on a idea Combined nonlinear filtering architectures involving sparse functional link adaptive filters"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Sparsity phenomena in learning processes have been extensively studied, since their detection allows to derive suited regularized optimization algorithms capable of improving the overall learning performance. ,  investigate the sparsity behavior that may occur in nonlinear adaptive filtering problems and how to leverage it and develop enhanced algorithms. In particular,  focus on a particular class of linear-in-the-parameters nonlinear adaptive filters, whose nonlinear transformation is based on a functional link expansion. The analysis of the sparsity in functional links leads us to derive a family of adaptive combined filtering architectures that is capable of exploiting any sparseness degree in the nonlinear filtering.  propose two different filtering schemes based on a new block-based combined approach, ll suited for sparse adaptive algorithms. Moreover, a hierarchical architecture is also proposed that generalizes the different combined schemes and does not need any a priori information about the nature of the nonlinearity to be modeled. Experimental results prove the effectiveness of the proposed combined architectures in exploiting any sparseness degree and improving the modeling performance in nonlinear system identification problems."}]