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- W3146738188 abstract "柴油机引擎的吸入系统是一个强壮的非线性的系统，并且建立吸入系统的精确模型是困难的；并且柴油机引擎斜面的地图的偏爱差错和精确降级差错用基于模型的方法容易被诊断。因此，一个差错诊断方法基于 Elman 神经网络观察员被建议。由基于 BP 网络和 Elman 神经网络比较吸入压力的模拟结果，更低的采样错误大小用 Elman 神经网络被获得，并且错误是不太不稳定的。预报精确性在 0.015-0.017 之间 5 并且样品错误在 0-0.07 以内被控制。就输出稳定性和包括地解决的复杂性而言，有单个隐藏的层并且与 44 个节点的 Elman 神经网络表现为吸入系统观察员。由比较在测量并且预言的价值之间的剩余价值的信心间隔的关系，在各种各样的差错的错误变化和失败打字。然后，柴油机引擎的四个典型地图差错能被诊断：完成失败差错，偏爱差错，精确降级差错和飘移差错。模拟结果出现：吸入压力是看得见的，诊断策略参数的选择能相当增加诊断的精确性；建议差错诊断方法仅仅取决于数据和观察员的结构的参数，不依靠空气吸入的非线性的模特儿系统。一个差错诊断方法被建议观察吸入压力的不看情况的系统模型，和偏爱差错和精确降级地图指责柴油机引擎能基于剩余被诊断。" @default.
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- W3146738188 date "2016-01-01" @default.
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- W3146738188 title "Fault Diagnosis for Manifold Absolute Pressure Sensor（MAP） of Diesel Engine Based on Elman Neural Network Observer" @default.
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