A fuzzy neighborhood-based training algorithm for feedforward neural networks

被引：0
|
作者：
Mounir Ben Nasr
Mohamed Chtourou
机构：
[1] Research Unit on Intelligent Control,Department of Electrical Engineering
[2] Design and Optimization of Complex Systems (ICOS),undefined
[3] ENIS,undefined
来源：
Neural Computing and Applications | 2009年 / 18卷
关键词：
Feedforward neural network; Gradient descent algorithm; Supervised and unsupervised learning; Fuzzy self-organizing feature map; Hybrid training;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this work we present a new hybrid algorithm for feedforward neural networks, which combines unsupervised and supervised learning. In this approach, we use a Kohonen algorithm with a fuzzy neighborhood for training the weights of the hidden layers and gradient descent method for training the weights of the output layer. The goal of this method is to assist the existing variable learning rate algorithms. Simulation results show the effectiveness of the proposed algorithm compared with other well-known learning methods.
引用
收藏
页码：127 / 133
页数：6
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