臺灣博碩士論文加值系統

為了解決線性或後非線性獨立成份分析問題，我們洐生了這個新方法。傳統獨立成份分析是以最小化重新得到的獨立成份元素間的KL值為基礎，有別於傳統獨立成份分析，這個新方法使用了週期性的最佳化後非線性解方法來解決線性或後非線性獨立成份的訊號

A new method is devised for linear and post-nonlinear independent component analysis. Unlike traditional statistics oriented ICA algorithms, which have been developed based on minimization of the Kullback-Leibler(KL) divergence between retrieved components, this method uses the recurrent optimal post-nonlinear kernel method to realize blind separation of linear or post-nonlinear mixtures of independent sources. The post-nonlinear mixing structure of independent sources is realized by multiple generalized adalines(gadalines). Following the leave-one-out learning strategy, hyper-parameters of each gadaline as well as intermediate independent components are optimized under a mean-field-annealing process to satisfy constraints given by multi-channel observations. The new method is shown more feasible for post-nonlinear independent component analysis. It indeed involves with no computations of optimizing statistics, such as the KL divergence or high order moments, which have been known diffcult to be resolved for nonlinear independent component analysis.

1 Introduction............................................1
2 Post-nonlinear mixtures of independent sources..........2
2.1 A weighted gadaline.................................2
2.2 Multiple weighted gadalines.........................3
3 Leave-one-out learning for post-nonlinear
independentcomponent analysis...........................4
3.1 Leave-one-out approximation.........................4
3.2 Optimal gadaline....................................4
3.3 Compensation for uncertainty........................8
3.4 A recurrent learning process for post-nonlinear
independent component analysis......................9
4 Numerical simulations..................................11
4.1 Post-nonlinear mixtures of arti…cial independent
sources............................................11
4.2 Blind separation of fetal ECGs.....................12
5 Conclusions............................................13

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