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| A class of gradient algorithms with variable learning rates and convergence analysis for feedforward neural networks training |
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SHAO Hong mei1, AN Feng xian2
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(1.School of Mathematics and Computational Science in China University of Petroleum, Dongying 257061, China;2.Department of Computer Science, Huaiyin Institute of Technology, Huaian 223003, China)
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| Abstract: |
| A general updating rule for learning rates was presented and the convergence of the corresponding batch gradient algorithms with variable learning rates for training feedforward neural networks was proved. The monotonicity of the error function in the training iteration was also proved. |
| Key words: feedforward neural networks convergence variable learning rate gradient algorithm |
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