Gradient descent learning algorithm for hierarchical neural networks: A case study in industrial quality

被引：0

作者：

Baratta, D ^{[1
]}

Diotalevi, F ^{[1
]}

Valle, M ^{[1
]}

Caviglia, DD ^{[1
]}

机构：

[1] Univ Genoa, Dept Biophys & Elect Engn, I-16145 Genoa, Italy

来源：

ENGINEERING APPLICATIONS OF BIO-INSPIRED ARTIFICIAL NEURAL NETWORKS, VOL II | 1999年 / 1607卷

关键词：

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper deals with the training procedure for a hierarchical neural network (Tree of Multi-Layer Perceptrons - TMLP) aimed to classify surface defects in flat rolled strips. Due to the difficulties in collecting large Data Bases it is necessary to exploit at the best the available knowledge. A comparison between techniques derived from both the Back-Propagation and Weight-Perturbation algorithms is done, and experimental results are reported.

引用

页码：578 / 587

页数：10

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