一種優(yōu)化動量因子的盲分離算法論文-閱讀頁
2025-07-04 05:49本頁面
【正文】 ，RLS 算法比LMS算法分離性能好。在線盲源分離仿真實驗結(jié)果驗證了算法的可實現(xiàn)性，并同時仿真了傳統(tǒng)LMS算法和RLS算法。c Grove, USA: IEEE, 1998. 119111954 Ou S F, Zhao X H, Gao Y. Variable step size technique for adaptive blind decorrelation. In: Proceedings of the 8th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing. Qingdao, China: IEEE, 2007. 8238265 Yuan L X, Wang W W, Chambers J A. Variable stepsize sign natural gradient algorithm for sequential blind source separation. IEEE Signal Processing Letters, 2005, 12(8):5895926 Hsieh S T, Sun T Y, Lin C L, Liu C C. Effective learning rate adjustment of blind source separation based on an improved particle swarm optimizer. IEEE Transactions on Evolutionary Computation, 2008, 12(2): 2422517 ArenasGarcia J, GomezVerdejo V, FigueirasVidal A R. New algorithms for improved adaptive convex bination of LMS transversal flters. IEEE Transactions on Instrumentation and Measurement, 2005, 54(6): 223922498 X L Zhu,et al. A fast NPCA algorithm for online blind source separation[J].Neuroputing,2006,69(79):964968.9 S. C. Douglas, SelfStabilized Gradient Algorithms for Blind Source Separation with Orthogonality Constraints, IEEE Trans. Neural Networks 11(6)(2000)1490149710 B. Yang, “Projection approximation subspace tracking,” IEEE Trans. Signal Processing, vol. 43, pp. 95–107, Jan. 199
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