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Method and device for conducting a process in a controlled system with at least one precomputed parameter based on a plurality of results from partial mathematical models combined by a neural network

机译:用于基于来自神经网络组合的部分数学模型的多个结果在具有至少一个预先计算的参数的受控系统中进行过程的方法和设备

摘要

In known methods for conducting a process in an automatically controlled system, the system is preset at the beginning of each process run according to at least one process parameter. The at least one process parameter is precomputed with a model of the process which is supplied with input values. During the process, the input values and the process parameters are measured and are used after the process run to adaptively improve the precomputed value of the process parameters. To simplify and improve the precomputed value of a model having a plurality of partial models, computed results of the partial models are supplied to a neural network. The neural network produces the process parameters to be precomputed as a network response. The network parameters of the neural network are modified after each process run to adapt the precomputed value to the actual process events.
机译:在用于在自动控制的系统中进行过程的已知方法中,该系统根据至少一个过程参数在每个过程运行的开始时被预设。所述至少一个过程参数用提供有输入值的过程模型预先计算。在过程中,将测量输入值和过程参数,并在过程运行后使用这些输入值和过程参数,以自适应地提高过程参数的预先计算的值。为了简化和改善具有多个局部模型的模型的预先计算的值,将局部模型的计算结果提供给神经网络。神经网络产生要预先计算为网络响应的过程参数。在每个过程运行之后,都会修改神经网络的网络参数,以使预先计算的值适应实际过程事件。

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