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- W2982261090 abstract "The echo state networks (ESNs) have been widely used for time series prediction, due to their excellent learning performance and fast convergence speed. However, the obtained output weight of ESN by pseudoinverse is always ill-posed. In order to solve this problem, the ESN with batch gradient method and smoothing (ell _0) regularization (ESN-BGSL0) is studied. By introducing a smooth (ell _0) regularizer into the traditional error function, some redundant output weights of ESN-BGSL0 are driven to zeros and pruned. Two examples are performed to illustrate the efficiency of the proposed algorithm in terms of estimation accuracy and network compactness." @default.
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- W2982261090 date "2019-01-01" @default.
- W2982261090 modified "2023-10-08" @default.
- W2982261090 title "Batch Gradient Training Method with Smoothing $$l_0$$ Regularization for Echo State Networks" @default.
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- W2982261090 doi "https://doi.org/10.1007/978-3-030-32388-2_42" @default.
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