Estimating source kurtosis directly from observation data for ICA
Signal Processing
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A parameterized activation function in the form of an adaptive threshold for a single-layer neural network, which separates a mixture of signals with any distribution (except for Gaussian), is introduced. This activation function is particularly simple to implement, since it neither uses hyperbolic nor polynomial functions, unlike most other nonlinear functions used for blind separation. For some specific distributions, the stable region of the threshold parameter is derived, and optimal values for best separation performance are given. If the threshold parameter is made adaptive during the separation process, the successful separation of signals whose distribution is unknown is demonstrated and compared against other known methods