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1. 发明授权

US08566264B2 Method for the computer-assisted control and/or regulation of a technical system 有权
标题翻译：计算机辅助控制和/或调节技术系统的方法
公开(公告)号：US08566264B2
公开(公告)日：2013-10-22
申请号：US12521920
申请日：2007-12-19
申请人： Anton Maximilian Schäfer , Steffen Udluft
发明人： Anton Maximilian Schäfer , Steffen Udluft
IPC分类号： G06E1/00 , G06E3/00 , G06F15/18 , G06G7/00 , G06N3/02
CPC分类号： G05B13/027 , G05B2219/33025 , G05B2219/33036 , G06N3/08
摘要： A method for the computer-assisted control and/or regulation of a technical system is provided. The method is used to efficiently reduce a high-dimensional state space describing the technical system to a smaller dimension. The reduction of the state space is performed using an artificial recurrent neuronal network. In addition, the reduction of the state space enables conventional learning methods, which are only designed for small dimensions of state spaces, to be applied to complex technical systems with an initially large state space, wherein the conventional learning methods are performed in the reduced state space. The method can be used with any technical system, especially gas turbines.
摘要翻译：提供了一种用于计算机辅助控制和/或调节技术系统的方法。该方法用于将描述技术系统的高维状态空间有效地降低到更小的维度。使用人工复发神经元网络来执行状态空间的减少。此外，状态空间的减少使得仅能够设计用于状态空间的小尺寸的常规学习方法被应用于具有最初大的状态空间的复杂技术系统，其中传统的学习方法以简化的状态执行空间。该方法可用于任何技术系统，特别是燃气轮机。

2. 发明授权

US08260441B2 Method for computer-supported control and/or regulation of a technical system 有权
标题翻译：计算机支持的技术系统控制和/或调节方法
公开(公告)号：US08260441B2
公开(公告)日：2012-09-04
申请号：US12595092
申请日：2008-04-04
申请人： Daniel Scheegaβ , Steffen Udluft
发明人： Daniel Scheegaβ , Steffen Udluft
IPC分类号： G06F19/00
CPC分类号： G05B13/027
摘要： A method for computer-supported control and/or regulation of a technical system is provided. In the method a reinforcing learning method and an artificial neuronal network are used. In a preferred embodiment, parallel feed-forward networks are connected together such that the global architecture meets an optimal criterion. The network thus approximates the observed benefits as predictor for the expected benefits. In this manner, actual observations are used in an optimal manner to determine a quality function. The quality function obtained intrinsically from the network provides the optimal action selection rule for the given control problem. The method may be applied to any technical system for regulation or control. A preferred field of application is the regulation or control of turbines, in particular a gas turbine.
摘要翻译：提供了一种用于计算机支持的控制和/或调节技术系统的方法。在该方法中，使用加强学习方法和人工神经元网络。在优选实施例中，并行前馈网络连接在一起，使得全球架构满足最佳标准。因此，网络将观察到的收益近似为预期效益的预测因子。以这种方式，以最佳方式使用实际观察来确定质量函数。从网络本质上获得的质量函数为给定的控制问题提供了最佳动作选择规则。该方法可以应用于任何用于调节或控制的技术系统。优选的应用领域是涡轮机，特别是燃气轮机的调节或控制。

3. 发明授权

US08250014B2 Method for the computer-aided learning of a control or adjustment of a technical system using a quality function and training data 有权
标题翻译：使用质量函数和培训数据控制或调整技术系统的计算机辅助学习方法
公开(公告)号：US08250014B2
公开(公告)日：2012-08-21
申请号：US12386638
申请日：2009-04-21
申请人： Daniel Schneegaβ , Steffen Udluft
发明人： Daniel Schneegaβ , Steffen Udluft
IPC分类号： G06F17/00
CPC分类号： G05B13/0265
摘要： A method for the computer-aided learning of a control of a technical system is provided. An operation of the technical system is characterized by states which the technical system can assume during operation. Actions are executed during the operation and convert a relevant state into a subsequent state. The method is characterized in that, when learning the control, suitable consideration is given to the statistical uncertainty of the training data. This is achieved in that the statistical uncertainty of a quality function which models an optimal operation of the technical system is specified by an uncertainty propagation and is incorporated into an action selection rule when learning. By a correspondingly selectable certainty parameter, the learning method can be adapted to different application scenarios which vary in statistical requirements. The method can be used for learning the control of an operation of a turbine, in particular a gas turbine.
摘要翻译：提供了一种用于技术系统的控制的计算机辅助学习的方法。技术系统的操作的特点是技术系统在运行期间可以承担的状态。在操作期间执行动作，并将相关状态转换为后续状态。该方法的特征在于，当学习控制时，适当考虑训练数据的统计不确定性。这是通过不确定性传播来规定对技术系统的最佳操作建模的质量函数的统计不确定性来实现的，并且在学习时被并入动作选择规则中。通过相应可选择的确定性参数，学习方法可以适应于在统计需求中不同的不同应用场景。该方法可以用于学习对涡轮机，特别是燃气轮机的操作的控制。

4. 发明申请

US20090271344A1 Method for computer-aided control or regualtion of a technical system 有权
标题翻译：计算机辅助控制或技术系统规范的方法
公开(公告)号：US20090271344A1
公开(公告)日：2009-10-29
申请号：US12386639
申请日：2009-04-21
申请人： Anton Maximillian Schafer , Volkmar Sterzing , Steffen Udluft
发明人： Anton Maximillian Schafer , Volkmar Sterzing , Steffen Udluft
IPC分类号： G06N3/08
CPC分类号： G05B13/027
摘要： A method for computer-aided control of any technical system is provided. The method includes two steps, the learning of the dynamic with historical data based on a recurrent neural network and a subsequent learning of an optimal regulation by coupling the recurrent neural network to a further neural network. The recurrent neural network has a hidden layer comprising a first and a second hidden state at a respective time point. The first hidden state is coupled to the second hidden state using a matrix to be learned. This allows a bottleneck structure to be created, in that the dimension of the first hidden state is smaller than the dimension of the second hidden state or vice versa. The autonomous dynamic is taken into account during the learning of the network, thereby improving the approximation capacity of the network. The technical system includes a gas turbine.
摘要翻译：提供了一种用于任何技术系统的计算机辅助控制的方法。该方法包括两个步骤：基于循环神经网络学习具有历史数据的动态，以及通过将再循环神经网络耦合到另一神经网络来进一步学习最佳调节。循环神经网络具有在相应时间点包括第一和第二隐藏状态的隐藏层。使用要学习的矩阵将第一隐藏状态耦合到第二隐藏状态。这允许创建瓶颈结构，因为第一隐藏状态的维度小于第二隐藏状态的维度，反之亦然。在网络学习期间考虑自主动态，从而提高网络的近似能力。技术系统包括燃气轮机。

5. 发明授权

US09489619B2 Method for the computer-assisted modeling of a technical system 有权
标题翻译：技术系统的计算机辅助建模方法
公开(公告)号：US09489619B2
公开(公告)日：2016-11-08
申请号：US13992799
申请日：2011-11-16
申请人： Siegmund Düll , Alexander Hans , Steffen Udluft
发明人： Siegmund Düll , Alexander Hans , Steffen Udluft
IPC分类号： G06F15/18 , G06N3/08 , G06N3/04
CPC分类号： G06N3/08 , G06N3/0445
摘要： A method for computer-assisted modeling of a technical system is disclosed. At multiple different operating points, the technical system is described by a first state vector with first state variable(s) and by a second state vector with second state variable(s). A neural network comprising a special form of a feed-forward network is used for the computer-assisted modeling of said system. The feed-forward network includes at least one bridging connector that connects a neural layer with an output layer, thereby bridging at least one hidden layer, which allows the training of networks with multiple hidden layers in a simple manner with known learning methods, e.g., the gradient descent method. The method may be used for modeling a gas turbine system, in which a neural network trained using the method may be used to estimate or predict nitrogen oxide or carbon monoxide emissions or parameters relating to combustion chamber vibrations.
摘要翻译：公开了一种技术系统的计算机辅助建模方法。在多个不同的操作点，技术系统由具有第一状态变量的第一状态向量和具有第二状态变量的第二状态向量描述。包括前馈网络的特殊形式的神经网络用于所述系统的计算机辅助建模。前馈网络包括至少一个桥接连接器，其将神经层与输出层相连，从而桥接至少一个隐藏层，这允许以已知的学习方法以简单的方式训练具有多个隐藏层的网络，例如，梯度下降法。该方法可以用于对燃气轮机系统进行建模，其中使用该方法训练的神经网络可以用于估计或预测氮氧化物或一氧化碳排放物或与燃烧室振动相关的参数。

6. 发明申请

US20130282635A1 Method For The Computer-Assisted Modeling Of A Technical System 有权
标题翻译：计算机辅助建模技术系统的方法
公开(公告)号：US20130282635A1
公开(公告)日：2013-10-24
申请号：US13992799
申请日：2011-11-16
申请人： Siegmund Düll , Alexander Hans , Steffen Udluft
发明人： Siegmund Düll , Alexander Hans , Steffen Udluft
IPC分类号： G06N3/08
CPC分类号： G06N3/08 , G06N3/0445
摘要： A method for computer-assisted modeling of a technical system is disclosed. At multiple different operating points, the technical system is described by a first state vector with first state variable(s) and by a second state vector with second state variable(s). A neural network comprising a special form of a feed-forward network is used for the computer-assisted modeling of said system. The feed-forward network includes at least one bridging connector that connects a neural layer with an output layer, thereby bridging at least one hidden layer, which allows the training of networks with multiple hidden layers in a simple manner with known learning methods, e.g., the gradient descent method. The method may be used for modeling a gas turbine system, in which a neural network trained using the method may be used to estimate or predict nitrogen oxide or carbon monoxide emissions or parameters relating to combustion chamber vibrations.
摘要翻译：公开了一种技术系统的计算机辅助建模方法。在多个不同的操作点，技术系统由具有第一状态变量的第一状态向量和具有第二状态变量的第二状态向量描述。包括前馈网络的特殊形式的神经网络用于所述系统的计算机辅助建模。前馈网络包括至少一个桥接连接器，其将神经层与输出层相连，从而桥接至少一个隐藏层，这允许以已知的学习方法以简单的方式训练具有多个隐藏层的网络，例如，梯度下降法。该方法可以用于对燃气轮机系统进行建模，其中使用该方法训练的神经网络可以用于估计或预测氮氧化物或一氧化碳排放物或与燃烧室振动相关的参数。

7. 发明申请

US20100241243A1 Method for the Computer-Assisted Exploration of States of a Technical System 有权
标题翻译：计算机辅助探索技术系统国家的方法
公开(公告)号：US20100241243A1
公开(公告)日：2010-09-23
申请号：US12740500
申请日：2008-09-29
申请人： Alexander Hans , Daniel Schneegass , Anton Maximilian Schäfer , Volkmar Sterzing , Steffen Udluft
发明人： Alexander Hans , Daniel Schneegass , Anton Maximilian Schäfer , Volkmar Sterzing , Steffen Udluft
IPC分类号： G05B13/02 , G06F15/18 , G05B9/02
CPC分类号： G05B13/026 , F23N2023/40 , F23N2041/20 , G05B13/027 , G06N99/005
摘要： A method for the computer-assisted exploration of states of a technical system is provided. The states of the technical system are run by carrying out an action in a respective state of the technical system, the action leading to a new state. A safety function and a feedback rule are used to ensure that a large volume of data of states and actions is run during exploration and that at the same time no inadmissible actions occur which could lead directly or indirectly to the technical system being damaged or to a defective operating state. The method allows a large number of states and actions relating to the technical system to be collected and may be used for any technical system, especially the exploration of states in a gas turbine. The method may be used both in the real operation and during simulation of the operation of a technical system.
摘要翻译：提供了一种用于计算机辅助探索技术系统状态的方法。技术系统的状态是通过在技术系统的各自的状态下执行动作，导致新的状态的动作。使用安全功能和反馈规则来确保在勘探期间运行大量的状态和动作数据，并且同时不会发生不可接受的行为，这些行为可能直接或间接地导致技术系统被损坏或者导致操作状态不良该方法允许收集与技术系统相关的大量状态和动作，并且可以用于任何技术系统，特别是对燃气轮机状态的探索。该方法可以在实际操作中和在模拟技术系统的操作期间使用。

8. 发明授权

US08380646B2 Method for the computer-assisted learning of a control and/or a feedback control of a technical system using a modified quality function and a covariance matrix 有权
标题翻译：使用修改的质量函数和协方差矩阵的计算机辅助学习控制和/或技术系统的反馈控制的方法
公开(公告)号：US08380646B2
公开(公告)日：2013-02-19
申请号：US12875159
申请日：2010-09-03
申请人： Alexander Hans , Steffen Udluft
发明人： Alexander Hans , Steffen Udluft
IPC分类号： G06F15/18
CPC分类号： G05B13/0265
摘要： A method of computer-assisted learning of control and/or feedback control of a technical system is provided. A statistical uncertainty of training data used during learning is suitably taken into account when learning control of the technical system. The statistical uncertainty of a quality function, which models an optimal operation of the technical system, is determined by uncertainty propagation and is incorporated during learning of an action-selecting rule. The uncertainty propagation uses a covariance matrix in which non-diagonal elements are ignored. The method can be used for learning control or feedback control of any desired technical systems. In a variant, the method is used for control or feedback control of an operation of a gas turbine. In another variant, the method is used for control or feedback control of a wind power plant.
摘要翻译：提供了一种计算机辅助学习技术系统的控制和/或反馈控制的方法。在学习技术系统的控制时，适当考虑到在学习期间使用的训练数据的统计学不确定性。质量函数的统计不确定性，其对技术系统的最佳操作进行建模，由不确定性传播确定，并且在学习动作选择规则期间被并入。不确定度传播使用协方差矩阵，其中非对角线元素被忽略。该方法可用于任何所需技术系统的学习控制或反馈控制。在一个变型中，该方法用于燃气轮机的操作的控制或反馈控制。在另一个变型中，该方法用于风力发电厂的控制或反馈控制。

9. 发明申请

US20130013543A1 METHOD FOR THE COMPUTER-AIDED CONTROL OF A TECHNICAL SYSTEM 审中-公开
标题翻译：一种技术系统的计算机辅助控制方法
公开(公告)号：US20130013543A1
公开(公告)日：2013-01-10
申请号：US13583057
申请日：2011-02-15
申请人： Siegmund Düll , Volkmar Sterzing , Steffen Udluft
发明人： Siegmund Düll , Volkmar Sterzing , Steffen Udluft
IPC分类号： G06N3/08
CPC分类号： G05B13/027
摘要： A method for the computer-aided control of a technical system is provided. A recurrent neuronal network is used for modeling the dynamic behaviour of the technical system, the input layer of which contains states of the technical system and actions carried out on the technical system, which are supplied to a recurrent hidden layer. The output layer of the recurrent neuronal network is represented by an evaluation signal which reproduces the dynamics of technical system. The hidden states generated using the recurrent neural network are used to control the technical system on the basis of a learning and/or optimization method.
摘要翻译：提供了一种用于技术系统的计算机辅助控制的方法。复发神经元网络用于对技术系统的动态行为进行建模，其技术系统的输入层包含技术系统的状态和在技术系统上执行的动作，并提供给复发隐藏层。复发神经元网络的输出层由再现动态技术系统的评估信号表示。使用循环神经网络产生的隐藏状态用于在学习和/或优化方法的基础上控制技术系统。

10. 发明申请

US20100094788A1 Method for the computer-assisted control and/or regulation of a technical system 失效
标题翻译：计算机辅助控制和/或调节技术系统的方法
公开(公告)号：US20100094788A1
公开(公告)日：2010-04-15
申请号：US12522040
申请日：2007-12-19
申请人： Anton Maximilian Schäfer , Steffen Udluft , Hans-Georg Zimmerman
发明人： Anton Maximilian Schäfer , Steffen Udluft , Hans-Georg Zimmerman
IPC分类号： G06N3/08 , G05B13/04
CPC分类号： G05B13/027 , G05B2219/33011 , G05B2219/33021 , G05B2219/33025 , G05B2219/33036 , G06N3/02
摘要： A method for the computer-assisted control and/or regulation of a technical system is provided. The method includes two steps, namely learning the dynamics of a technical system using historical data based on a recurrent neuronal network, and the subsequent learning of an optimum regulation by coupling the recurrent neuronal network to another neuronal network. The method can be used with any technical system in order to control the system in an optimum computer-assisted manner. For example, the method can be used in the control of a gas turbine.
摘要翻译：提供了一种用于计算机辅助控制和/或调节技术系统的方法。该方法包括两个步骤，即使用基于复发性神经元网络的历史数据来学习技术系统的动力学，以及随后通过将复发性神经元网络耦合到另一神经元网络来学习最佳调节。该方法可以与任何技术系统一起使用，以便以最佳的计算机辅助方式控制系统。例如，该方法可用于燃气轮机的控制。

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