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1. 发明授权

US5563983A Learning system operated through a layered neural network 失效
标题翻译：学习系统通过分层神经网络运行
公开(公告)号：US5563983A
公开(公告)日：1996-10-08
申请号：US436602
申请日：1995-05-08
申请人： Chikanori Nozaki , Junko Murai , Yoshihiro Nakatani
发明人： Chikanori Nozaki , Junko Murai , Yoshihiro Nakatani
IPC分类号： G06F15/18 , G06G7/60 , G06N3/04 , G06N3/08 , G06N99/00
CPC分类号： G06N3/049 , Y10S706/926
摘要： The present invention predicts an output result in response to unknown input data using a layered neural network. For example, learning data collected in time series with rate of changes included are learned through the layered neural network. As a result, a predicted value can be obtained with a smaller amount of learning data within a shorter learning time. The present invention comprises a rate of change calculating unit for calculating the rate of change of time-series data at two different time points, and a network generating unit for controlling the learning steps performed by the layered neural network using at least the rate of change of data as learning data so as to establish a neural network in which a weight value is determined for learning data. As a predicting system, it further comprises at least a neural network recognizing unit for applying to the neural network established by the network generating unit an output of the rate of change calculating unit outputted in response to input predicting data, and for obtaining a prediction result.
摘要翻译：本发明使用分层神经网络来预测响应未知输入数据的输出结果。例如，通过分层神经网络学习以时间序列收集的学习数据，包括变化率。结果，可以在更短的学习时间内用较少量的学习数据来获得预测值。本发明包括用于计算两个不同时间点上的时间序列数据的变化率的变化率计算单元，以及用于使用至少变化率来控制由分层神经网络执行的学习步骤的网络生成单元的数据作为学习数据，以便建立其中为学习数据确定权重值的神经网络。作为预测系统，还包括至少一个神经网络识别单元，用于向由网络生成单元建立的神经网络应用响应于输入预测数据输出的变化率计算单元的输出，并且用于获得预测结果。

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