Convergence of hybrid algorithm with adaptive learning parameter for multilayer neural network

被引：0
|
作者：
Damak, Fadwa [1 ]
Ben Nasr, Mounir [1 ]
Chtourou, Mohamed [1 ]
机构：
[1] ENIS, Dept Elect Engn, Sfax, Tunisia
来源：
WORLD CONGRESS ON COMPUTER & INFORMATION TECHNOLOGY (WCCIT 2013) | 2013年
关键词：
Feedforward; neural network; Hybrid training; Adaptive learning rate; Lyapunov theory; TRAINING ALGORITHM; MOMENTUM;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
A new learning algorithm suited for training multilayered neural networks that we have named hybrid is hereby introduced. With this algorithm the weights of the hidden layer are adjusted using the Kohonen algorithm. While the weights of the output layer are trained using a gradient descent method with adaptive learning parameter based Lyapunov function. The effectiveness of the proposed approach is shown by the simulation results.
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页数：4
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