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Title:
OUTLIER DETECTION DEVICE, OUTLIER DETECTION METHOD, AND OUTLIER DETECTION PROGRAM
Document Type and Number:
WIPO Patent Application WO/2020/194591
Kind Code:
A1
Abstract:
This outlier detection device is provided with: a reservoir computer which includes an input layer, a reservoir main unit including a plurality of neurons connected to each other by synapses, and a readout that calculates and outputs the inner product of a weight vector and an activity value vector having, as elements thereof, activity values that have been output by each of the plurality of neurons on the basis of inputs to the input layer; a learning unit which acquires an observed signal, calculates an error between the inner product and the observed signal, and updates the weight vector using an value obtained by applying an adaptive filter to the error; a norm calculation unit which calculates the norm of the weight vector each time the weight vector is updated by the learning unit; and a determination unit which determines whether or not the observed signal includes an outlier, on the basis of at least one of the norms calculated by the norm calculation unit.

Inventors:
NAKADA KAZUKI (JP)
Application Number:
PCT/JP2019/013320
Publication Date:
October 01, 2020
Filing Date:
March 27, 2019
Export Citation:
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Assignee:
TDK CORP (JP)
International Classes:
G05B23/02; G06N3/08; G06N3/04
Foreign References:
US20140108094A12014-04-17
Other References:
RENATO DUARTE , PEGGY SERIES , ABIGAIL MORRISON: "Self-Organized Artificial Grammar Learning in Spiking Neural Networks", PROCEEDINGS OF THE ANNUAL MEETING OF THE COGNITIVE SCIENCE SOCIETY, vol. 36, 2014, pages 427 - 432, XP055743341, ISSN: 1069-7977
VICTORIA HODGE; JIM AUSTIN: "A survey of outlier detection methodologies", ARTIFICIAL INTELLIGENCE REVIEW, vol. 22, no. 2, 31 October 2004 (2004-10-31), pages 85 - 126, XP055176642, ISSN: 0269-2821, DOI: 10.1023/B:AIRE.0000045502.10941.a9
Attorney, Agent or Firm:
TANAI Sumio et al. (JP)
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