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Liquid gauging apparatus using a time delay neural network

机译:使用时延神经网络的液体计量装置

摘要

Liquid gauging apparatus using a time delay neural network for determining a quantity of liquid in a container that is not directly measurable by sensors is disclosed. The apparatus comprises a plurality of sensors and a processor. Each of the sensors are capable of measuring a respective parameter of the liquid and for producing a time varying sensor output signal representative of the respective parameter measured thereby. The processor is programmed to process the sensor output signals by a time delay neural network algorithm to determine a current quantity of the liquid in the container based on current and past parameter measurements of the sensor output signals. Also disclosed is a method of training a time delay neural network algorithm for computing a quantity of liquid in a container from current and past liquid parameter sensor measurements. The method comprises the steps of: establishing a dynamic model of liquid behavior in the container and parameter measurements of the liquid behavior sensed by a plurality of sensors; deriving from the dynamic model training data sets for a plurality of liquid quantity values, each data set comprising current and past liquid parameter sensor measurement values corresponding to a liquid quantity value of the plurality, and the corresponding liquid quantity value; and training the time delay neural network algorithm with the derived training data sets.
机译:公开了一种使用时间延迟神经网络的液体计量装置,该液体计量装置用于确定容器中不能由传感器直接测量的液体量。该设备包括多个传感器和处理器。每个传感器都能够测量液体的相应参数,并且能够产生代表由此测量的相应参数的时变传感器输出信号。处理器被编程为通过时延神经网络算法处理传感器输出信号,以基于传感器输出信号的当前和过去的参数测量值来确定容器中液体的当前量。还公开了一种训练时间延迟神经网络算法的方法,该算法用于根据当前和过去的液体参数传感器的测量结果计算容器中的液体量。该方法包括以下步骤:建立容器中液体行为的动态模型和由多个传感器感测的液体行为的参数测量;从动态模型训练数据集获得多个液体量值,每个数据集包括对应于多个液体量值的当前和过去液体参数传感器测量值,以及对应的液体量值;利用导出的训练数据集训练时延神经网络算法。

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