Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  introduce linear autoassociative neural (AN) network filters for the removal of additive noise from one-dimensional (1-D) time series. The AN network will have a (2M+1)/spl times/L/spl times/(2M+1) architecture, and for M fixed,  show how to choose the optimal L value and output coordinate from square error estimates beten the AN filter outputs and the clean series. The frequency response of AN filters are also studied, and they are shown to act as matched band filters. A noise variance estimate is also derived from this analysis.  numerically illustrate their behavior on two examples and also compare their theoretical performance with that of optimal Wiener filters.
['José R. Dorronsoro', 'Vicente López', 'Carlos Santa Cruz', 'Juan A. Sigüenza']
I am writing a research paper on a idea Autoassociative neural networks and noise filtering
[{"from": "human", "value": "I am writing a research paper on a idea Autoassociative neural networks and noise filtering"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  introduce linear autoassociative neural (AN) network filters for the removal of additive noise from one-dimensional (1-D) time series. The AN network will have a (2M+1)/spl times/L/spl times/(2M+1) architecture, and for M fixed,  show how to choose the optimal L value and output coordinate from square error estimates beten the AN filter outputs and the clean series. The frequency response of AN filters are also studied, and they are shown to act as matched band filters. A noise variance estimate is also derived from this analysis.  numerically illustrate their behavior on two examples and also compare their theoretical performance with that of optimal Wiener filters."}]