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Adaptive basis function for artificial neural networks
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AI
DOI：10.1016/S0925-2312(01)00578-1.png)
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摘要
En 中文
It is shown that modifying the sigmoidal basis function of a multi-layer feedforward artificial neural network using a control parameter improves the network's ability to learn. The modification is rendered by a gradient descent algorithm similar to the back-propagation. In doing so, the method retains all the goodies of the sigmoidal function and causes the ANN to approximate the decision function faster and also with better accuracy. (C) 2002 Elsevier Science B.V. All rights reserved.
Keyword:
back propagation algorithm
neural networks
optimization of basis function
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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暂无机构信息
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