Abstract
A neural-based method for source separation in nonlinear mixture is proposed in this paper. A cost function, which consists of the mutual information and partial moments of the outputs of the separation system, is defined to extract the independent signals from their nonlinear mixtures. A learning algorithm for the parametric RBF network is established by using the stochastic gradient descent method. This approach is characterized by high learning convergence rate of weights, modular structure, as well as feasible hardware implementation. Successful experimental results are given at the end of this paper.
Keywords
- Independent Component Analysis
- Independent Component Analysis
- Radial Basis Function Network
- Blind Source Separation
- Blind Signal
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Tan, Y. (2007). Neural-Based Separating Method for Nonlinear Mixtures. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_87
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DOI: https://doi.org/10.1007/978-3-540-72395-0_87
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