Optimization Neural Network for Blind Signal Separation Using an Adaptive Weibull Distribution
نویسندگان
چکیده
Annotation: In this papre We propose a neural network optimization algorithm for independent component analysis(ICA) which can separate mixtures of suband superGaussian source signals with self-adaptive nonlinearities. The ICA algorithem in the framework of natural Riemannian gradient, is derived using the parameterized Weibull density model. The nonlinear function in ICA algorithem is self-adaptive and is controlled by the shape parameter of Weibull density model. Computer simulation results confirm the validity and high performance of the proposed algorithm
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تاریخ انتشار 2006