WO2007020466A3 - Data classification apparatus and method - Google Patents

Data classification apparatus and method Download PDF

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Publication number
WO2007020466A3
WO2007020466A3 PCT/GB2006/003111 GB2006003111W WO2007020466A3 WO 2007020466 A3 WO2007020466 A3 WO 2007020466A3 GB 2006003111 W GB2006003111 W GB 2006003111W WO 2007020466 A3 WO2007020466 A3 WO 2007020466A3
Authority
WO
WIPO (PCT)
Prior art keywords
classification
data
classification apparatus
network
data classification
Prior art date
Application number
PCT/GB2006/003111
Other languages
French (fr)
Other versions
WO2007020466A2 (en
Inventor
Christopher Kirkham
Roberta Cambio
Helge Nareid
Original Assignee
Axeon Ltd
Christopher Kirkham
Roberta Cambio
Helge Nareid
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from GB0517033A external-priority patent/GB0517033D0/en
Priority claimed from GB0517009A external-priority patent/GB0517009D0/en
Application filed by Axeon Ltd, Christopher Kirkham, Roberta Cambio, Helge Nareid filed Critical Axeon Ltd
Publication of WO2007020466A2 publication Critical patent/WO2007020466A2/en
Publication of WO2007020466A3 publication Critical patent/WO2007020466A3/en

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/2433—Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02D—CONTROLLING COMBUSTION ENGINES
    • F02D41/00—Electrical control of supply of combustible mixture or its constituents
    • F02D41/02—Circuit arrangements for generating control signals
    • F02D41/14—Introducing closed-loop corrections
    • F02D41/1401—Introducing closed-loop corrections characterised by the control or regulation method
    • F02D41/1405—Neural network control
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02D—CONTROLLING COMBUSTION ENGINES
    • F02D41/00—Electrical control of supply of combustible mixture or its constituents
    • F02D41/02—Circuit arrangements for generating control signals
    • F02D41/18—Circuit arrangements for generating control signals by measuring intake air flow

Abstract

The present invention relates to data classification apparatus (10). The data classification apparatus (10) comprises a data processor (13) operative to present a classification input, which corresponds to data to be classified, to each of a plurality of neural networks (15). Each neural network of the plurality of neural networks (15) has a different response characteristic corresponding to a different, predetermined classification, with each neural network being operable to produce a network output in dependence upon the neural network's response characteristic and the received classification input. The data classification apparatus also has a classification processor (17) operable to receive a network output from each neural network of the plurality of neural networks (15) and, in dependence upon at least one received network output, to determine if the classification input belongs to at least one of the plurality of different, predetermined classifications.
PCT/GB2006/003111 2005-08-19 2006-08-18 Data classification apparatus and method WO2007020466A2 (en)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
GB0517033A GB0517033D0 (en) 2005-08-19 2005-08-19 Method and apparatus for data classification and change detection
GB0517033.7 2005-08-19
GB0517009.7 2005-08-19
GB0517009A GB0517009D0 (en) 2005-08-19 2005-08-19 Apparatus and method for function estimation

Publications (2)

Publication Number Publication Date
WO2007020466A2 WO2007020466A2 (en) 2007-02-22
WO2007020466A3 true WO2007020466A3 (en) 2007-11-01

Family

ID=37654791

Family Applications (2)

Application Number Title Priority Date Filing Date
PCT/GB2006/003093 WO2007020456A2 (en) 2005-08-19 2006-08-18 Neural network method and apparatus
PCT/GB2006/003111 WO2007020466A2 (en) 2005-08-19 2006-08-18 Data classification apparatus and method

Family Applications Before (1)

Application Number Title Priority Date Filing Date
PCT/GB2006/003093 WO2007020456A2 (en) 2005-08-19 2006-08-18 Neural network method and apparatus

Country Status (1)

Country Link
WO (2) WO2007020456A2 (en)

Families Citing this family (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2085593B1 (en) 2008-01-29 2010-06-30 Honda Motor Co., Ltd. Control system for internal combustion engine
DE602008001660D1 (en) 2008-01-29 2010-08-12 Honda Motor Co Ltd Control system for an internal combustion engine
US9053433B2 (en) 2010-07-06 2015-06-09 Bae Systems, Plc Assisting vehicle guidance over terrain
WO2017058133A1 (en) * 2015-09-28 2017-04-06 General Electric Company Apparatus and methods for allocating and indicating engine control authority
US10260407B2 (en) 2016-02-03 2019-04-16 Cummins Inc. Gas quality virtual sensor for an internal combustion engine
WO2019084556A1 (en) * 2017-10-27 2019-05-02 Google Llc Increasing security of neural networks by discretizing neural network inputs
GB201719587D0 (en) * 2017-11-24 2018-01-10 Sage Global Services Ltd Method and apparatus for determining an association
CN111832342A (en) * 2019-04-16 2020-10-27 阿里巴巴集团控股有限公司 Neural network, training and using method, device, electronic equipment and medium
CN115879350A (en) * 2023-02-07 2023-03-31 华中科技大学 Aircraft resistance coefficient prediction method based on sequential sampling

Citations (3)

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Publication number Priority date Publication date Assignee Title
US5303330A (en) * 1991-06-03 1994-04-12 Bell Communications Research, Inc. Hybrid multi-layer neural networks
US6292738B1 (en) * 2000-01-19 2001-09-18 Ford Global Tech., Inc. Method for adaptive detection of engine misfire
US20040034611A1 (en) * 2002-08-13 2004-02-19 Samsung Electronics Co., Ltd. Face recognition method using artificial neural network and apparatus thereof

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2792633B2 (en) * 1990-02-09 1998-09-03 株式会社日立製作所 Control device
US6236908B1 (en) * 1997-05-07 2001-05-22 Ford Global Technologies, Inc. Virtual vehicle sensors based on neural networks trained using data generated by simulation models
GB9902115D0 (en) * 1999-02-01 1999-03-24 Axeon Limited Neural networks
GB0204826D0 (en) * 2002-03-01 2002-04-17 Axeon Ltd Control of a mechanical actuator using a modular map processor

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5303330A (en) * 1991-06-03 1994-04-12 Bell Communications Research, Inc. Hybrid multi-layer neural networks
US6292738B1 (en) * 2000-01-19 2001-09-18 Ford Global Tech., Inc. Method for adaptive detection of engine misfire
US20040034611A1 (en) * 2002-08-13 2004-02-19 Samsung Electronics Co., Ltd. Face recognition method using artificial neural network and apparatus thereof

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
CONNY BERGKVIST AND STEFAN WIKNER: "Self-organizing maps for virtual sensors, fault detection and fault isolation in diesel engines", 9 March 2005, LINKÖPING UNIVERSITY, DEPARTMENT OF ELECTRICAL ENGINEERING, INSTITUTIONEN FÖR SYSTEMTEKNIK 581 83 LINKÖPING, SWEDEN, XP002444956 *
DITTENBACH M ET AL: "The growing hierarchical self-organizing map", NEURAL NETWORKS, 2000. IJCNN 2000, PROCEEDINGS OF THE IEEE-INNS-ENNS INTERNATIONAL JOINT CONFERENCE ON 24-27 JULY 2000, PISCATAWAY, NJ, USA,IEEE, vol. 6, 24 July 2000 (2000-07-24), pages 15 - 19, XP010504958, ISBN: 0-7695-0619-4 *
LIGHTOWLER, N.; SPRACKLEN, C.T.; ALLEN, A.: "A Modular Approach to Implementation of the Self Organising Map", PROC. OF THE WORKSHOP ON SELF ORGANISING MAPS, June 1997 (1997-06-01), Helsinki University of Technology, Finland, pages 130 - 135, XP002444953 *
P. NEIL: "Combining a hardware neural network with a powerful automotive MCU for powertrain applications", INDUSTRIAL EMBEDDED SYSTEMS, vol. 1, no. 1, October 2005 (2005-10-01), pages 88 - 89, XP002444954 *

Also Published As

Publication number Publication date
WO2007020456A3 (en) 2007-08-16
WO2007020466A2 (en) 2007-02-22
WO2007020456A2 (en) 2007-02-22

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