首页> 外国专利> METHOD AND DEVICE FOR DISCOVERING IMPORTANT WEIGHT OF NEURAL NET AND STORAGE MEDIUM WITH STORED IMPORTANT WEIGHT DISCOVERY PROGRAM FOR NEURAL NET

METHOD AND DEVICE FOR DISCOVERING IMPORTANT WEIGHT OF NEURAL NET AND STORAGE MEDIUM WITH STORED IMPORTANT WEIGHT DISCOVERY PROGRAM FOR NEURAL NET

机译:用存储的神经网络重要重量发现程序发现神经网络和存储介质的重要重量的方法和装置

摘要

PROBLEM TO BE SOLVED: To provide a method and a device for discovering important weight of neural net and a storage medium with stored important weight discovery program for neural net, which can discover a numerical rule with a clear important weight even when plural unwanted weights are included in a neural net. ;SOLUTION: The weight of the neural net and a penalty coefficient for every weight are initialized, a weight for minimizing a learning target function with square value penalty term is found while using a secondary learning method, a weight for verifying a crossing and updating the penalty coefficient is found while using the secondary learning method, a search direction for updating the penalty coefficient is analytically found from the provided weight on the basis of the theorem of a shadow function, a search width for updating the penalty coefficient is found on the base of a Gauss-Newton method, and the penalty coefficient is updated while using the secondary learning method.;COPYRIGHT: (C)2001,JPO
机译:解决的问题:提供一种用于发现神经网络的重要权重的方法和装置以及一种存储有用于神经网络的重要权重发现程序的存储介质,该程序可以发现具有清晰的重要权重的数值规则,即使存在多个不必要的权重包含在神经网络中。 ;解决方案:初始化神经网络的权重和每个权重的惩罚系数,在使用二次学习方法的同时找到用于最小化具有平方值惩罚项的学习目标函数的权重,用于验证交叉和更新权重的权重在使用二次学习方法的同时找到惩罚系数,基于阴影函数定理从提供的权重中解析地找到用于更新惩罚系数的搜索方向,在此基础上找到用于更新惩罚系数的搜索宽度高斯-牛顿法的改进,并在使用二次学习法的同时更新惩罚系数。;版权所有:(C)2001,日本特许厅

著录项

  • 公开/公告号JP2001142864A

    专利类型

  • 公开/公告日2001-05-25

    原文格式PDF

  • 申请/专利权人 NIPPON TELEGR & TELEPH CORP NTT;

    申请/专利号JP19990324312

  • 发明设计人 SAITO KAZUMI;

    申请日1999-11-15

  • 分类号G06F15/18;

  • 国家 JP

  • 入库时间 2022-08-22 01:29:25

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