An efficient model for systems with complex responses (neural network architecture for nonlinear filtering)

Presents a neural network architecture for a restricted class of nonlinear filtering applications. The filter architecture is particularly suited for biomedical and technical applications that require long and complex system responses. The filter architecture was successfully used in a biomedical application for the removal of the cardiac interference from magnetoencephalographic (MEG) data and performed better than standard linear filters and the time-delay neural network.<
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