基于模糊神经网络的汽油机怠速控制

Logesh Velmurugan
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引用次数: 0

摘要

模糊神经网络是一种基于状态反馈的智能方法。在学习和识别的过程中,它具有非常高的容错率,可以帮助人们快速准确地找到自己需要的信息。采用模糊神经网络控制方法对发动机怠速和燃油燃烧过程进行了测量。因此,为了提高汽油机怠速控制能力,本文研究了模糊神经网络在汽油机怠速控制系统中的应用。本文主要采用实验方法,通过不同时期车辆怠速停车的变量,对基于多模式的燃油经济性进行比较分析。实验结果表明,该系统在高峰时段的燃油经济性平均提高了20.38%。基于多模态信息的怠速启停控制策略可以有效地提高启停系统的燃油经济性。
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Idle Speed Control of Gasoline Engine Based on Fuzzy Neural Network
: Fuzzy neural network is an intelligent method based on state feedback. In the process of learning and recognition, it has a very high fault tolerance rate and can help people quickly and accurately find the information they need. The engine idle speed and fuel combustion process are measured by using the fuzzy neural network control method. Therefore, in order to improve the idle speed control ability of gasoline engine, this paper studies the application of fuzzy neural network in its control system. This paper mainly uses the experimental method to compare and analyze the fuel economy based on multi-mode through the variables of vehicle idle parking in different periods. The experimental results show that the fuel economy of the start stop system in peak hours is increased by 20.38% on average. The idle start stop control strategy based on multi-mode information can effectively improve the fuel economy of the start stop system.
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