WO2001015078A3 - Verfahren zum trainieren eines neuronalen netzes - Google Patents

Verfahren zum trainieren eines neuronalen netzes Download PDF

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Publication number
WO2001015078A3
WO2001015078A3 PCT/EP2000/008280 EP0008280W WO0115078A3 WO 2001015078 A3 WO2001015078 A3 WO 2001015078A3 EP 0008280 W EP0008280 W EP 0008280W WO 0115078 A3 WO0115078 A3 WO 0115078A3
Authority
WO
WIPO (PCT)
Prior art keywords
neural network
neurons
training
predetermined
synapses
Prior art date
Application number
PCT/EP2000/008280
Other languages
English (en)
French (fr)
Other versions
WO2001015078A2 (de
Inventor
Ronald Kates
Nadia Harbeck
Manfred Schmitt
Original Assignee
Wilex Ag
Ronald Kates
Nadia Harbeck
Manfred Schmitt
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
Application filed by Wilex Ag, Ronald Kates, Nadia Harbeck, Manfred Schmitt filed Critical Wilex Ag
Priority to AU74130/00A priority Critical patent/AU7413000A/en
Priority to US10/049,650 priority patent/US6968327B1/en
Priority to AT00962377T priority patent/ATE516558T1/de
Priority to EP00962377A priority patent/EP1232478B1/de
Publication of WO2001015078A2 publication Critical patent/WO2001015078A2/de
Publication of WO2001015078A3 publication Critical patent/WO2001015078A3/de

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    • 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
    • G06N3/08—Learning methods
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/41—Detecting, measuring or recording for evaluating the immune or lymphatic systems
    • A61B5/414—Evaluating particular organs or parts of the immune or lymphatic systems
    • A61B5/415—Evaluating particular organs or parts of the immune or lymphatic systems the glands, e.g. tonsils, adenoids or thymus
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/41—Detecting, measuring or recording for evaluating the immune or lymphatic systems
    • A61B5/414—Evaluating particular organs or parts of the immune or lymphatic systems
    • A61B5/418—Evaluating particular organs or parts of the immune or lymphatic systems lymph vessels, ducts or nodes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Abstract

Das erfindungsgemäße Verfahren dient zum Trainieren eines neuronalen Netzes zur Ermittlung von Risikofunktionen für Patienten im Anschluß an eine Ersterkrankung mit einer vorbestimmten Krankheit auf Grundlage vorgegebener Trainings-Datensätze, welche objektivierbare und meßtechnisch erfaßte Daten zum Krankheitsbild der Patienten beinhalten. Das neuronale Netz umfaßt eine Mehrzahl von in mehreren Schichten angeordneten Neuronen sowie diese Neuronen verbindende Synapsen. Im Verlaufe des Trainings wird die Struktur des neuronalen Netzes vereinfacht, indem Synapsen aufgespürt und eliminiert werden, welche auf den Verlauf der Risikofunktion keinen wesentlichen Einfluß ausüben. Dies kann beispielsweise dadurch erfolgen, daß man die Einflüsse, die zwei Sende-Neuronen auf ein und dasselbe Empfangs-Neuron ausüben, auf eine mögliche Korrelation untersucht und gegebenenfalls eine der beiden zu dem Empfangs-Neuron führenden Synapsen eliminiert.
PCT/EP2000/008280 1999-08-26 2000-08-24 Verfahren zum trainieren eines neuronalen netzes WO2001015078A2 (de)

Priority Applications (4)

Application Number Priority Date Filing Date Title
AU74130/00A AU7413000A (en) 1999-08-26 2000-08-24 Method for training a neural network
US10/049,650 US6968327B1 (en) 1999-08-26 2000-08-24 Method for training a neural network
AT00962377T ATE516558T1 (de) 1999-08-26 2000-08-24 Verfahren zum trainieren eines neuronalen netzes
EP00962377A EP1232478B1 (de) 1999-08-26 2000-08-24 Verfahren zum trainieren eines neuronalen netzes

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE19940577.8 1999-08-26
DE19940577A DE19940577A1 (de) 1999-08-26 1999-08-26 Verfahren zum Trainieren eines neuronalen Netzes

Publications (2)

Publication Number Publication Date
WO2001015078A2 WO2001015078A2 (de) 2001-03-01
WO2001015078A3 true WO2001015078A3 (de) 2002-06-27

Family

ID=7919734

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/EP2000/008280 WO2001015078A2 (de) 1999-08-26 2000-08-24 Verfahren zum trainieren eines neuronalen netzes

Country Status (6)

Country Link
US (1) US6968327B1 (de)
EP (1) EP1232478B1 (de)
AT (1) ATE516558T1 (de)
AU (1) AU7413000A (de)
DE (1) DE19940577A1 (de)
WO (1) WO2001015078A2 (de)

Cited By (1)

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CN106339755A (zh) * 2016-08-29 2017-01-18 深圳市计量质量检测研究院 基于神经网络与周期核函数gpr的锂电池健康状态预测方法

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US7395248B2 (en) * 2000-12-07 2008-07-01 Kates Ronald E Method for determining competing risks
US6917926B2 (en) * 2001-06-15 2005-07-12 Medical Scientists, Inc. Machine learning method
WO2004061548A2 (en) * 2003-01-07 2004-07-22 Ramot At Tel Aviv University Ltd. Identification of effective elements in complex systems
DE10345440A1 (de) * 2003-09-30 2005-05-12 Siemens Ag Verfahren, Computerprogramm mit Programmcode-Mitteln und Computerprogramm-Produkt zur Analyse von Einflussgrößen auf einen Brennvorgang in einer Brennkammer unter Verwendung eines trainierbaren, statistischen Modells
DE102004033614A1 (de) * 2004-07-12 2006-02-09 Emedics Gmbh Einrichtung und Verfahren zum Abschätzen einer Auftretenswahrscheinlichkeit einer Gesundheitsstörung
DE102007008514A1 (de) * 2007-02-21 2008-09-04 Siemens Ag Verfahren und Vorrichtung zur neuronalen Steuerung und/oder Regelung
US7814038B1 (en) 2007-12-06 2010-10-12 Dominic John Repici Feedback-tolerant method and device producing weight-adjustment factors for pre-synaptic neurons in artificial neural networks
US20090276385A1 (en) * 2008-04-30 2009-11-05 Stanley Hill Artificial-Neural-Networks Training Artificial-Neural-Networks
US9015096B2 (en) 2012-05-30 2015-04-21 Qualcomm Incorporated Continuous time spiking neural network event-based simulation that schedules co-pending events using an indexable list of nodes
WO2020033594A1 (en) * 2018-08-07 2020-02-13 Yale University Interpretable deep machine learning for clinical radiology
CN114207675A (zh) 2019-05-28 2022-03-18 佩治人工智能公司 用于数字病理学的用于处理图像以针对所处理的图像制备载片的系统和方法
CN113884903B (zh) * 2021-10-19 2023-08-18 中国计量大学 一种基于多层感知器神经网络的电池故障诊断方法

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US5528700A (en) * 1990-04-04 1996-06-18 Yozan Inc. Character recognition system based on a neural network
US5687286A (en) * 1992-11-02 1997-11-11 Bar-Yam; Yaneer Neural networks with subdivision
US5734797A (en) * 1996-08-23 1998-03-31 The United States Of America As Represented By The Secretary Of The Navy System and method for determining class discrimination features
US5812992A (en) * 1995-05-24 1998-09-22 David Sarnoff Research Center Inc. Method and system for training a neural network with adaptive weight updating and adaptive pruning in principal component space

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US6601051B1 (en) * 1993-08-09 2003-07-29 Maryland Technology Corporation Neural systems with range reducers and/or extenders
US6594629B1 (en) * 1999-08-06 2003-07-15 International Business Machines Corporation Methods and apparatus for audio-visual speech detection and recognition

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5528700A (en) * 1990-04-04 1996-06-18 Yozan Inc. Character recognition system based on a neural network
US5687286A (en) * 1992-11-02 1997-11-11 Bar-Yam; Yaneer Neural networks with subdivision
US5812992A (en) * 1995-05-24 1998-09-22 David Sarnoff Research Center Inc. Method and system for training a neural network with adaptive weight updating and adaptive pruning in principal component space
US5734797A (en) * 1996-08-23 1998-03-31 The United States Of America As Represented By The Secretary Of The Navy System and method for determining class discrimination features

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106339755A (zh) * 2016-08-29 2017-01-18 深圳市计量质量检测研究院 基于神经网络与周期核函数gpr的锂电池健康状态预测方法
CN106339755B (zh) * 2016-08-29 2018-09-21 深圳市计量质量检测研究院 基于神经网络与周期核函数gpr的锂电池健康状态预测方法

Also Published As

Publication number Publication date
AU7413000A (en) 2001-03-19
DE19940577A1 (de) 2001-03-01
EP1232478A2 (de) 2002-08-21
EP1232478B1 (de) 2011-07-13
US6968327B1 (en) 2005-11-22
ATE516558T1 (de) 2011-07-15
WO2001015078A2 (de) 2001-03-01

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