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SI Engine Emissions Model Based on Dynamic Neural Networks and D-Optimality

机译：基于动态神经网络和D-最优性的SI发动机排放模型

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摘要

In the last two decades the abilities of neural networks as universal approximation tools of non linear functional relationships as well as identification tools for nonlinear dynamic systems have been recognized and used successfully in many applications areas like modelling, control and diagnosis of technical systems. At the same time an increasing interest in optimal design methods is observed. Design of experiment is used to cope with the growing amount of measurements needed for the calibration of engines due to the rising number of control variables to be considered and the need for more accuracy in the description of engine behavior to derive the best control strategies. In this paper a strategy for the integration of the concept of D-optimality in the learning process of neural networks is proposed. This leads to an optimal selection of data to be presented to the training procedure of the neural network aiming to a generation of robust neural models using fewer training data. An application example dealing with the modelling of the emissions of an SI engine illustrates the successful use of the proposed concept. The generated emissions model is real time capable so that it can be used as a virtual sensor for ECU control and diagnosis functions.
机译：在过去的几十年中，神经网络作为非线性功能关系的通用逼近工具的能力以及非线性动态系统的识别工具已经被认可，并且在许多应用领域成功地使用了技术系统的建模，控制和诊断。同时观察到对最佳设计方法的越来越大的兴趣。实验设计用于应对引擎校准所需的越来越多的测量值，由于要考虑的控制变量的数量升高，并且在发动机行为的描述中需要更准确的准确度来获得最佳控制策略。在本文中，提出了一种集成D-Operality概念在神经网络的学习过程中的策略。这导致了最佳选择，以呈现给神经网络的培训程序，旨在使用较少的训练数据产生强大的神经模型。处理SI引擎排放的建模的应用示例说明了所提出的概念的成功使用。生成的排放模型是实时的能力，使其可以用作ECU控制和诊断功能的虚拟传感器。

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