A pattern recognition neural network using many sets of weights and biases

Dung Le, Makoto Mizukawa

研究成果: Conference contribution

4 被引用数 (Scopus)

抄録

In supervised training, we often try to find out a set of weights and biases for a pattern recognition neural network in order to classify all patterns in a training data set. However, it would be difficult if the neural network was not big enough for learning a large training data set. In this paper, we propose a training method and a design of pattern recognition neural network that is not big but still able to classify all the training patterns exactly. The neural network is designed with a reject output to separate the training data set into some parts for classifying more easily. The training method helps the neural network to find out not only one but many sets of weights and biases for classifying all the training patterns, controlling the recognizing rejection and reducing the error rate. On the other hand, with this design we can reduce the size of the neural network implemented on a FPGA chip in order to make fast smart sensors for the robots.

本文言語English
ホスト出版物のタイトルProceedings of the 2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2007
ページ285-290
ページ数6
DOI
出版ステータスPublished - 2007
外部発表はい
イベント2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2007 - Jacksonville, FL, United States
継続期間: 2007 6月 202007 6月 23

出版物シリーズ

名前Proceedings of the 2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2007

Conference

Conference2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2007
国/地域United States
CityJacksonville, FL
Period07/6/2007/6/23

ASJC Scopus subject areas

  • 人工知能
  • ソフトウェア
  • 制御およびシステム工学
  • 電子工学および電気工学

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