A Batch Variable Learning Rate Gradient Descent Algorithm With the Smoothing L1/2 Regularization for Takagi-Sugeno Models
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DOI: 10.1109/access.2020.2997867
Abstract: A batch variable learning rate gradient descent algorithm is proposed to efficiently train a neuro-fuzzy network of zero-order Takagi-Sugeno inference systems. By using the advantages of regularization, the smoothing $L_{1/2}$ regularization is utilized to find… read more here.
Keywords: rate; rate gradient; algorithm; learning rate ... See more keywords