H∞ Filtering in Neural Network Training and Pruning with Application to System Identification-Reference-Cited by-同舟云学术

H∞ Filtering in Neural Network Training and Pruning with Application to System Identification

Published:2007-01 Issue:1 Volume:21 Page:47-58
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Tang He-Sheng¹²³,Xue Songtao¹²³,Sato Tadanobu¹²³

Affiliation:

1. Lecturer, Research Institute of Structural Engineering and Disaster Reduction, Tongji Univ., Shanghai 200092, China. E-mail: thstj@mail.tongji.edu.cn

2. Associate Professor, Dept. of Architecture, School of Science and Engineering, Kinki Univ., Kowakae 3-4-1, Higashi Osaka City 577-0056, Japan.

3. Professor, Disaster Prevention Research Institute, Kyoto Univ, Uji, Kyoto 611-0011, Japan.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Computer Science Applications,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%290887-3801%282007%2921%3A1%2847%29

Reference38 articles.

1. A fast feedforward training algorithm using a modified form of the standard backpropagation algorithm

2. Investigating the role of saliency analysis with a neural network rainfall-runoff model

3. A new look at the statistical model identification

4. Coupling weight elimination with genetic algorithms to reduce network size and preserve generalization

5. Fast Second Order Learning Algorithm for Feedforward Multilayer Neural Networks and its Applications

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