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首页> 外文期刊>IEEE / ASME Transactions on Mechatronics >A machine learning approach to modeling and identification of automotive three-way catalytic converters
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A machine learning approach to modeling and identification of automotive three-way catalytic converters

机译:一种用于汽车三元催化转化器建模和识别的机器学习方法

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摘要

The working of three-way catalytic converters (TWCs) is based on chemical reactions whose rates are nonlinear functions of temperature and reactant concentrations all along the device. The choice of suitable expressions and the tuning of their parameters is particularly difficult in dynamic conditions. In this paper, we introduce a hybrid modeling technique which allows one to preserve the most important features of an accurate, distributed parameter TWC model, while it circumvents both the structural and the parameter uncertainties of "classical" reaction kinetics models, and saves the computational time; in particular, we compute the rates within the TWC dynamic model by a neural network which becomes a static nonlinear component of a larger dynamic system. A purposely designed genetic algorithm, in conjunction with a fast ad hoc partial differential equation integration procedure, allows one to train the neural network, embedded in the whole model structure, using currently available measurement data and without computing gradient information.
机译:三元催化转化器(TWC)的工作基于化学反应,其速率是整个装置中温度和反应物浓度的非线性函数。在动态条件下,选择合适的表达式及其参数的调整特别困难。在本文中,我们介绍了一种混合建模技术,该技术可以保留精确的分布式参数TWC模型的最重要特征,同时规避了“经典”反应动力学模型的结构和参数不确定性,并节省了计算量时间;特别是,我们通过神经网络计算TWC动态模型中的速率,该神经网络成为较大动态系统的静态非线性组件。一种经过专门设计的遗传算法,结合快速的特设偏微分方程积分程序,可以使用当前可用的测量数据来训练神经网络,并将其嵌入整个模型结构中,而无需计算梯度信息。

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