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The convex combination of two momentum term based algorithms with different momentum factors is an effective solution to highlight the tradeoff between convergence rate and steady-state error of a blind source separation system. As the smoothing factor is chosen in the range from 0 to 1, however, the performance of this convex combination is restricted. In this paper, a novel variable smoothing factor...
The nonlinear principal component analysis (NPCA) can be applied to solve the blind source separation (BSS) problem. By combining the optimum step size with the optimum momentum factor both derived by the decrement of the cost function of NPCA algorithm, an integration fast NPCA algorithm is proposed in this paper. Simulation experiments proved that the proposed algorithm is superior to other NPCA...
Combining higher order statistics that can identify non-minimum phase system with shift block method of blind separation (SHIBBS) algorithm which is good in separation and moderate amount of computation, an algorithm was proposed to identify and separate mixed sources for muti-input and muti-output (MIMO) system in frequency domain. However, the received signals have some disadvantages such as the...
Blind decorrelation is a related task to blind source separation (BSS) which is applicable to numerous problems. A critical challenge in adaptive blind decorrelation (ABD) is the choice of step size to achieve fast initial convergence speed and low steady state error in time-varying systems. Unfortunately, unlike some supervised training, the error factor of ABD is inaccessible in practice, and so...
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