Adaptive step-size natural gradient algorithm based on separating degree gradient
ABSTRACT This paper proposes to use separating degree to control the step-size of natural algorithm for the first time. After detailed analyzing relevant fixed step-size and variable step-size gradient algorithms, the paper presents a new adaptive step-size natural algorithm. Because the variability of the new algorithm's step-size is based on separating degree, its learning ratio is chosen adaptively according to separating degree, therefore it can improve convergence speed and reduce the misadjustment error in the steady state simultaneously. Computer simulations confirm the theoretical analysis and show the algorithm performance is superior to other natural algorithms.
Conference Paper: Blind signal separation for speech signals with noise[Show abstract] [Hide abstract]
ABSTRACT: Blind signal separation methods are diverse and almost studied in no noise situations, while the noise exists in practice. In this paper, firstly we designed an FIR filter and applied it to remove the added Gaussian noise in the observed signals. And then we improved the standard natural gradient algorithm (SNG) and named it as an improved natural gradient algorithm (ING). Lastly we used Fast ICA algorithm, the SNG and the ING to separate the mixed signals which were processed by the FIR filter. We contrasted and analogized the signal interference ration (SIR) of results of the three algorithms above. We obtained that it's helpful for separation to filter the observed signals under low signal to noise ration (SNR) circumstance, however under the high SNR circumstance, the filtering operation doesn't improve the separation property.2014 IEEE International Conference on Mechatronics and Automation (ICMA); 08/2014