Abstract
We present a novel algorithm for independent component analysis (ICA) based on gradient learning with simultaneous perturbation stochastic approximation (SPSA). This algorithm can work well both in batch mode and in on-line mode of ICA processing. It converges very fast even for non-stationary, and/or non-identically independent distributed (non-I.I.D.) signals, so that the algorithm is very suitable for most real-time applications. In this paper, theories and implementations of the algorithm are described. Results of computer simulation are also presented to demonstrate the effectiveness.
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Ding, S., Huang, J., Wei, D., Omata, S. (2004). Real-Time Independent Component Analysis Based on Gradient Learning with Simultaneous Perturbation Stochastic Approximation. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30133-2_47
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DOI: https://doi.org/10.1007/978-3-540-30133-2_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23206-3
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