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특허 상세정보

Supervised training of a neural network

국가/구분 United States(US) Patent 등록
국제특허분류(IPC7판) G06K-009/62    G06K-009/00   
미국특허분류(USC) 395/23 ; 395/22 ; 395/24 ; 395/27
출원번호 US-0176458 (1993-12-30)
발명자 / 주소
출원인 / 주소
인용정보 피인용 횟수 : 17  인용 특허 : 0

The present invention provides a system and method for supervised training of a neural network. A neural network architecture and training method is disclosed that is a modification of an ARTMAP architecture. The modified ARTMAP network is an efficient and robust paradigm which has the unique property of incremental supervised learning. Furthermore, the modified ARTMAP network has the capability of removing undesired knowledge that has previously been learned by the network.


A computer-implemented method of training and testing a modified ARTMAP neural network, wherein the modified ARTMAP neural network has an ART module that accepts an input pattern and has a first layer and a second layer, and a Map field, connected to said ART module, that accepts a target output pattern, the computer-implemented method comprising: (1) presenting an input pattern to be learned to the modified ARTMAP neural network; (2) determining a set of activations for the first layer; (3) determining a set of matching scores for the second layer; (4) ...

이 특허를 인용한 특허 피인용횟수: 17

  1. Francisco Jose Ayala. Adaptive neural learning system. USP2002076424961.
  2. Sarangapani Jagannathan ; Schricker David R.. Apparatus and method for diagnosing an engine using computer based models in combination with a neural network. USP2001056240343.
  3. Feldhake, Michael J.. Cognitive image filtering. USP2009087577631.
  4. Grichnik Anthony J.. Component machine testing using neural network processed vibration data analysis. USP1998125854993.
  5. Campos, Marcos M.; Yarmus, Joseph Sigmund. Intelligent sampling for neural network data mining models. USP2010127849032.
  6. Paquier, Williams J. F.. Method and apparatus for creating a pattern recognizer. USP2012108290250.
  7. Mol,Hendrik Anne; Van Nijen,Gerrit Cornelis. Method and sensor arrangement for load measurement on rolling element bearing. USP2008117444888.
  8. Ohura Kazutaka,JPX ; Matsubara Ryouji,JPX ; Kaneta Masahisa,JPX. Method for evaluating the faulted sections and states in a power transmission line. USP1998015712796.
  9. Ayala,Francisco J.. Method, system and computer program for developing cortical algorithms. USP2009027493295.
  10. Paquier, Williams J. F.. Multi-stage image pattern recognizer. USP2012048160354.
  11. Pescianschi, Dmitri. Neural network and method of neural network training. USP2017049619749.
  12. Pescianschi, Dmitri. Neural network and method of neural network training. USP2016079390373.
  13. Streit Roy L.. Neural network architecture for non-Gaussian components of a mixture density function. USP1998015712959.
  14. Harrison Gregory A.. Neural network based analysis system for vibration analysis and condition monitoring. USP2001106301572.
  15. Paquier, Williams J. F.. Neural network based pattern recognizer. USP2012078229209.
  16. Ayala,Francisco J.. System and method for developing artificial intelligence. USP2006117139740.
  17. Tsujino Hiroshi,JPX ; Koerner Edgar,JPX ; Masutani Tomohiko,JPX. System and method for image recognition. USP2001026185337.