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公开(公告)号：US20150134311A1
公开(公告)日：2015-05-14
申请号：US14075946
申请日：2013-11-08
IPC分类号： G06F19/00
摘要： Modeling effectiveness of a verum includes dividing a group of patients into a placebo group and a verum group, defining a plurality of characteristics of the group of patients, and generating a model for the placebo group based on the plurality of characteristics. The method also includes generating a model for the verum group based on the plurality of characteristics, and isolating a placebo effect in the verum group in order to determine a pure verum effect.
摘要翻译： 正确的建模功能包括将一组患者分成安慰剂组和正常组，定义患者组的多个特征，并且基于多个特征生成安慰剂组的模型。 该方法还包括基于多个特征为verum组生成模型，并且在verum组中分离安慰剂效应以便确定纯verum效应。
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2.发明授权Method for the computer-aided learning of a recurrent neural network for modeling a dynamic system 有权用于建模动态系统的循环神经网络的计算机辅助学习方法公开(公告)号：US09235800B2
公开(公告)日：2016-01-12
申请号：US13640543
申请日：2011-04-12
IPC分类号： G06N3/08
CPC分类号： G06N3/08
摘要： A method for the computer-aided learning of a recurrent neural network for modeling a dynamic system which is characterized at respective times by an observable vector with one or more observables as entries is provided. The neural network includes both a causal network with a flow of information that is directed forwards in time and a retro-causal network with a flow of information which is directed backwards in time. The states of the dynamic system are characterized by first state vectors in the causal network and by second state vectors in the retro-causal network, wherein the state vectors each contain observables for the dynamic system and also hidden states of the dynamic system. Both networks are linked to one another by a combination of the observables from the relevant first and second state vectors and are learned on the basis of training date including known observables vectors.
摘要翻译： 提供了一种用于对动态系统进行建模的循环神经网络的计算机辅助学习的方法，该动态系统在各自的时间由具有一个或多个可观察值的可观察向量表征为条目。 神经网络包括具有时间向前指向的信息流的因果网络和具有时间向后指向的信息流的复原因果网络。 动态系统的状态由因果网络中的第一状态向量和复原因果网络中的第二状态向量表征，其中状态向量每个都包含动态系统的可观察值，以及动态系统的隐藏状态。 两个网络通过相关的第一和第二状态向量的可观察的组合彼此相关联，并且基于包括已知的可观察向量的训练日期来学习。
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3.发明申请METHOD FOR THE COMPUTER-AIDED LEARNING OF A RECURRENT NEURAL NETWORK FOR MODELING A DYNAMIC SYSTEM 有权用于建模动态系统的复现神经网络的计算机辅助学习方法公开(公告)号：US20130204815A1
公开(公告)日：2013-08-08
申请号：US13640543
申请日：2011-04-12
IPC分类号： G06N3/08
CPC分类号： G06N3/08
摘要： A method for the computer-aided learning of a recurrent neural network for modeling a dynamic system which is characterized at respective times by an observable vector with one or more observables as entries is provided. The neural network includes both a causal network with a flow of information that is directed forwards in time and a retro-causal network with a flow of information which is directed backwards in time. The states of the dynamic system are characterized by first state vectors in the causal network and by second state vectors in the retro-causal network, wherein the state vectors each contain observables for the dynamic system and also hidden states of the dynamic system. Both networks are linked to one another by a combination of the observables from the relevant first and second state vectors and are learned on the basis of training date including known observables vectors.
摘要翻译： 提供了一种用于对动态系统进行建模的循环神经网络的计算机辅助学习的方法，该动态系统在各自的时间由具有一个或多个可观察值的可观察向量表征为条目。 神经网络包括具有时间向前指向的信息流的因果网络和具有时间向后指向的信息流的复原因果网络。 动态系统的状态由因果网络中的第一状态向量和复原因果网络中的第二状态向量表征，其中状态向量每个都包含动态系统的可观察值，以及动态系统的隐藏状态。 两个网络通过相关的第一和第二状态向量的可观察的组合彼此相关联，并且基于包括已知的可观察向量的训练日期来学习。
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公开(公告)号：US09618546B2
公开(公告)日：2017-04-11
申请号：US14114201
申请日：2012-04-25
IPC分类号： G01W1/10 , H02S50/10 , H02J3/38 , G01R21/133
CPC分类号： G01R21/133 , H02J3/383 , H02S50/10 , Y02E10/563
摘要： A method for improving the usability of photovoltaic installations (PV installations) by taking account of shading information of adjacent PV installations for forecasting the power output by a relevant PV installation is provided. In particular, cloud movements and cloud shapes are taken into account. This improves the accuracy of the forecast. Here, it is advantageous that short-term forecasts in relation to e.g. the next 15 minutes are possible and a substitute energy source can be activated accordingly, in good time, prior to a dip in the power output by the PV installation. The invention can be used e.g. in the field of renewable energies, PV installations or smart grids.
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5.发明申请公开(公告)号：US20150019276A1
公开(公告)日：2015-01-15
申请号：US14367914
申请日：2012-12-19
申请人： Per Egedal , Ralph Grothmann , Thomas Runkler , Volkmar Sterzing
发明人： Per Egedal , Ralph Grothmann , Thomas Runkler , Volkmar Sterzing
IPC分类号： G01R21/133 , G06N7/00 , G06N3/02
CPC分类号： G01R21/1333 , G06N3/02 , G06N3/0445 , G06N3/0454 , G06N7/005 , G06Q50/06 , H02J3/383 , H02J3/386 , H02J2003/003 , Y02E10/563 , Y02E10/763
摘要： A method for computer-assisted determination of usage of electrical energy produced by a power generation plant such as a renewable power generation plant is provided. The method uses a plurality of neural networks having a different structure or being learned differently for calculating future energy amounts produced by a power generation plant. To do so, the energy outputs of the power generation plant forecasted by the plurality of the neural networks are used to build histograms. Based on the histograms, energy amounts for different confidence levels describing the likelihood of the availability of the energy amount are determined, and different uses are assigned to different energy amounts. Energy amounts having a higher likelihood of availability in the future are sold at higher prices than other energy amounts.
摘要翻译： 提供了一种用于计算机辅助确定由可再生能源发电厂等发电厂生产的电能的使用的方法。 该方法使用具有不同结构或被不同学习的多个神经网络来计算由发电厂产生的未来能量。 为此，使用由多个神经网络预测的发电厂的能量输出来构建直方图。 基于直方图，确定描述能量可用性的可能性的不同置信水平的能量量，并且将不同的用途分配给不同的能量。 将来可能性较高的能源以比其他能源量更高的价格出售。
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公开(公告)号：US10521716B2
公开(公告)日：2019-12-31
申请号：US14849292
申请日：2015-09-09
摘要： Computer-assisted analysis of a data record from observations is provided. The data record contains, for each observation, a data vector that includes values of input variables and a value of a target variable. A neuron network structure is learned from differently initialized neuron networks based on the data record. The neuron networks respectively include an input layer, one or more hidden layers, and an output layer. The input layer includes at least a portion of the input variables, and the output layer includes the target variable. The neuron network structure outputs the mean value of the target variables of the output layers of the neuron networks. Sensitivity values are determined by the neuron network structure and stored. Each sensitivity value is assigned an observation and an input variable. The sensitivity value includes the derivative of the target variable of the assigned observation with respect to the assigned input variable.
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公开(公告)号：US10352973B2
公开(公告)日：2019-07-16
申请号：US14367914
申请日：2012-12-19
申请人： Per Egedal , Ralph Grothmann , Thomas Runkler , Volkmar Sterzing
发明人： Per Egedal , Ralph Grothmann , Thomas Runkler , Volkmar Sterzing
摘要： A method for computer-assisted determination of usage of electrical energy produced by a power generation plant such as a renewable power generation plant is provided. The method uses a plurality of neural networks having a different structure or being learned differently for calculating future energy amounts produced by a power generation plant. To do so, the energy outputs of the power generation plant forecasted by the plurality of the neural networks are used to build histograms. Based on the histograms, energy amounts for different confidence levels describing the likelihood of the availability of the energy amount are determined, and different uses are assigned to different energy amounts. Energy amounts having a higher likelihood of availability in the future are sold at higher prices than other energy amounts.
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公开(公告)号：US20160071006A1
公开(公告)日：2016-03-10
申请号：US14849292
申请日：2015-09-09
CPC分类号： G06N3/08 , G05B13/027 , G06N20/00
摘要： Computer-assisted analysis of a data record from observations is provided. The data record contains, for each observation, a data vector that includes values of input variables and a value of a target variable. A neuron network structure is learned from differently initialized neuron networks based on the data record. The neuron networks respectively include an input layer, one or more hidden layers, and an output layer. The input layer includes at least a portion of the input variables, and the output layer includes the target variable. The neuron network structure outputs the mean value of the target variables of the output layers of the neuron networks. Sensitivity values are determined by the neuron network structure and stored. Each sensitivity value is assigned an observation and an input variable. The sensitivity value includes the derivative of the target variable of the assigned observation with respect to the assigned input variable.
摘要翻译： 提供了从观察数据记录的计算机辅助分析。 对于每个观察，数据记录包含包含输入变量的值和目标变量的值的数据向量。 基于数据记录，从不同初始化的神经元网络中学习神经元网络结构。 神经元网络分别包括输入层，一个或多个隐藏层和输出层。 输入层包括输入变量的至少一部分，输出层包括目标变量。 神经网络结构输出神经元网络输出层目标变量的平均值。 灵敏度值由神经网络结构确定并存储。 每个灵敏度值都被分配一个观察值和一个输入变量。 灵敏度值包括相对于分配的输入变量的分配观察值的目标变量的导数。
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9.发明授权Method, computer program with program code means, and computer program product for determining a future behavior of a dynamic system 有权方法，具有程序代码的计算机程序，以及用于确定动态系统的未来行为的计算机程序产品公开(公告)号：US07464061B2
公开(公告)日：2008-12-09
申请号：US10558623
申请日：2004-04-07
CPC分类号： G05B13/027
摘要： An approximation is determined for the future system behavior by a similarity analysis using a previously known behavior of the dynamic system, whereupon the future system behavior is determined by using the approximation for the future behavior of the dynamic system as well as a neuronal network structure, especially a causal retro-causal network (causality analysis).
摘要翻译： 通过使用先前已知的动态系统的行为的相似性分析来确定未来系统行为的近似，由此通过使用动态系统的未来行为的近似以及神经元网络结构来确定未来的系统行为， 特别是因果复原因果网络（因果分析）。
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公开(公告)号：US10133981B2
公开(公告)日：2018-11-20
申请号：US14239313
申请日：2012-07-24
摘要： Disclosed is a method for the computer-assisted modeling of a technical system. One or more output vectors are modeled dependent on one or more input vectors by the learning process of a neural network on the basis of training data of known input vectors and output vectors. Each output vector comprises one or more operating variables of the technical system, and each input vector comprises one or more input variables that influence the operating variable(s). The neural network is a feedforward network with an input layer, a plurality of hidden layers, and an output layer. The output layer comprises a plurality of output clusters, each of which consists of one or more output neurons, the plurality of output clusters corresponding to the plurality of hidden layers. Each output cluster describes the same output vector and is connected to another hidden layer.
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