A Modified RLS Algorithm for ICA with Weighted Orthogonal Constraint
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Independent component analysis (ICA), as an important data processing technique, is widely employed in many areas. The objective of the ICA is to recover independent components from observed signals. Several algorithms, such as equivariant adaptive separation via independence algorithm, least-mean-square (LMS)-type algorithms and recursive least-squares (RLS)-type learning rules, are proposed to solve the ICA problem. In the present paper, a modified RLS algorithm for ICA with weighted orthogonal constraint is developed to implement source separation based on the local convergence analysis of the available algorithm. Comparative experiment results demonstrate that the proposed algorithm is better than existing learning rules in the aspect of the accuracy of separation and stability.
KeywordsIndependent component analysis Least-mean-square algorithm Recursive least-squares algorithm Weighted orthogonal constraint
This work was supported in part by the National Natural Science Foundation of China under Grant 61573014 and in part by the Fundamental Research Funds for the Central Universities of China under Grant JB180702.
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