Models in today's microcontrollers, e.g. engine control units, are realized with a multitude of characteristic curves and look-up tables. The increasing complexity of these models causes an exponential growth of t...
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ISBN:
(纸本)9783642165290
Models in today's microcontrollers, e.g. engine control units, are realized with a multitude of characteristic curves and look-up tables. The increasing complexity of these models causes an exponential growth of the required calibration memory. Hence, neural networks, e.g. multilayer perceptron networks (MLP), which provide a solution for this problem, become more important for modeling. Usually sigmoid functions are used as membership functions. The calculation of the therefore necessary exponential function is very demanding on low performance microcontrollers. Thus in this paper a modified activation function for the efficient implementation of MLP networks is proposed. Their advantages compared to standard look-up tables are illustrated by the application of an intake manifold model of a combustion engine.
Gear and its transmission are widely used in different transmission systems, and its complicated and changeable condition brings a series of problems to the fault feature extraction and diagnosis. In recent years, dee...
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Gear and its transmission are widely used in different transmission systems, and its complicated and changeable condition brings a series of problems to the fault feature extraction and diagnosis. In recent years, deep learning techniques have been gradually applied to feature extraction and pattern recognition, and the features of feature extraction and fault diagnosis in complex working environments have shown certain advantages. This study is based on stacked autoencoder under deep learning model, and improve training network performance by modified activation function. Through the network training before and after the experiment done, and to extract the fault feature data comparison in testing, improving network after activationfunction to extract fault features showed a greater advantage, can be a very good application in practical fault feature extraction.
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