Abstract—Traditional independent component analysis (ICA) method based on FastICA algorithm faced two main disadvantages. One is that the order of the independent components (ICs) is difficult to be determined and the other is that the FastICA algorithm often leads to local minimum solution, and the suitable source signals are not isolated. To alleviate these problems, an improved ICA algorithm based on artificial immune system (AIS) (called AIS-ICA) is presented. AIS is an attractive heuristic technique and has many advantages over other heuristic techniques such as it can be easily implemented and has great capability of escaping local optimal solutions The basic idea of the proposed AIS-ICA algorithm is to use AIS to determine the separating matrix of ICA. Simulation results from the artificial signal data illustrate the efficiency of the proposed AIS–ICA approach
Index Terms—Independent component analysis, artificial immune system, signal separation, heuristic algorithm.
Li-Yun Chen and Chi-Jie Lu are with Department of Industrial Management, Chien Hsin University of Science and Technology, Zhong-Li 320, Taoyuan, Taiwan (e-mail: email@example.com; firstname.lastname@example.org)
Cite:Li-Yuan Chen and Chi-Jie Lu, "An Improved Independent Component Analysis Algorithm Based on Artificial Immune System," International Journal of Machine Learning and Computing vol. 3, no. 1, pp. 93-97, 2013.