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Fuzzy-based learning rate determination for blind source separation
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AI
DOI：10.1109/TFUZZ.2003.812697.png)
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Abstract
En 中文
In recent years, many independent component analysis (ICA) algorithms have been proposed for blind source separation. These algorithms belong to the LMS-type algorithm in natural. Hence, the choice of the step-size reflects a tradeoff between misadjustment and the speed of convergence. Based on separation state of outputs of the neural network for ICA, this. paper develops a fuzzy inference-based step-size selection algorithm. The fuzzy inference system consists of two inputs (the second- and higher order correlation coefficients of output components) and one output (the fuzzy learning rate). In this way, the ICA algorithms become more efficient, which is verified by simulation results.
Keywords:
blind source separation (BSS)
fuzzy inference systems
neural computations
neural networks
statistical learning
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11.9
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5.0K
Citations:
2.9W
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