共查询到19条相似文献,搜索用时 46 毫秒
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在飞机设计与研制过程中,通过气动参数辨识建立可靠的飞行动力学模型非常重要。传统的气动参数辨识工程算法,诸如极大似然法,需要给出合理的飞行动力学模型以及待辨识参数的初值。基于传统神经网络的气动参数辨识可以避免飞行动力学建模过程,这种方法需要通过增量法、导数法间接地从神经网络提取气动参数。本文提出了一种基于物理信息神经网络的飞机气动参数辨识方法,可将含待辨识参数的飞行动力学模型作为正则项加入损失函数,直接辨识得到气动参数。该方法可以显著减少建模数据需求,也能提高建模精度。飞行仿真数据验证结果表明,该方法的无噪声、含2%噪声仿真数据,纵向飞行状态空间模型辨识最大相对误差分别为1.80%、4.64%,表明了基于物理信息神经网络的飞机气动参数辨识方法具有可行性,并对含噪声的飞行数据具有泛化性。 相似文献
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为提高ZPW-2000R轨道电路诊断系统的判定准确性和运行效率,提出了一种基于卷积神经网络的轨道电路运行状态智能识别方法。首先,根据轨道电路监测数据集构建轨道电路运行状态灰度图谱,以精准表达轨道电路的运行状态,并通过图像缩放建立实验样本;其次,构建卷积神经网络模型并对轨道电路运行状态灰度图谱进行特征提取与模式识别。实验结果表明,本文提出的方法对轨道电路运行状态识别的准确率为100%,可有效识别轨道电路正向占用状态、逆向占用状态和空闲状态。 相似文献
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本文通过模型非平面自由飞数值模拟和气动参数辨识研究在忽略非平面运动效应情况下参数辨识的可靠性和精度。 相似文献
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提出一种在飞机概念设计中基于参数辨识理论的设计参数分析方法。在给定的设计重量和任务剖面要求下,利用基于物理的运动方程和动力学方程模型辨识出飞机气动力参数,并在总体性能评估的基础上进行设计参数分析,给气动设计提供了设计参考;在辨识过程中针对参数可行域离散度很高使得很难收敛到Pareto解的问题,提出了将神经网络预测模型融合到遗传操作过程,使得尽量在可行域内搜索。最后通过对某客机进行算例分析,表明基于辨识理论的参数分析方法和改进的算法是合理和可行的。与一般经验公式方法相比,该方法对布局类型的限制较小,在满足概念设计参数分析准确度要求的条件下能够为下一步的气动设计提供设计指标。 相似文献
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Thrust estimation is a significant part of aeroengine thrust control systems. The traditional estimation methods are either low in accuracy or large in computation. To further improve the estimation effect, a thrust estimator based on Multi-layer Residual Temporal Convolutional Network(M-RTCN) is proposed. To solve the problem of dead Rectified Linear Unit(ReLU),the proposed method uses the Gaussian Error Linear Unit(GELU) activation function instead of ReLU in residual block. Then the overall a... 相似文献
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Rotating machinery is widely applied in industrial applications.Fault diagnosis of rotating machinery is vital in manufacturing system,which can prevent catastrophic failure and reduce financial losses.Recently,Deep Learning (DL)-based fault diagnosis method becomes a hot topic.Convolutional Neural Network (CNN) is an effective DL method to extract the features of raw data automatically.This paper develops a fault diagnosis method using CNN for InfRared Thermal(IRT) image.First,IRT technique is ... 相似文献
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二值卷积神经网络(BNN)占用存储空间小、计算效率高,然而由于网络前向的二值量化与反向梯度的不匹配问题,使其与同结构的全精度深度卷积神经网络(CNN)之间存在较大的性能差距,影响了其在资源受限平台上的部署。至今,研究者已提出了一系列网络设计与训练方法来降低卷积神经网络在二值化过程中的性能损失,以推动二值卷积神经网络在嵌入式便携设备发展中的应用。因此,本文对二值卷积神经网络进行综述,主要从提高网络表达能力与充分挖掘网络训练潜力两大方面,给出了当前二值卷积神经网络的发展脉络与研究现状。具体而言,提高网络表达能力分为二值化量化方法设计、结构设计两方面,充分挖掘网络训练潜力分为损失函数设计与训练策略两方面。最后,对二值卷积神经网络在不同任务与硬件平台的实验情况进行了总结和技术分析,并展望了未来研究中可能面临的挑战。 相似文献
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Fault diagnosis of bearings based on deep separable convolutional neural network and spatial dropout
Bearing pitting, one of the common faults in mechanical systems, is a research hotspot in both academia and industry. Traditional fault diagnosis methods for bearings are based on manual experience with low diagnostic efficiency. This study proposes a novel bearing fault diagnosis method based on deep separable convolution and spatial dropout regularization. Deep separable convolution extracts features from the raw bearing vibration signals, during which a 3 × 1 convolutional kernel with a one-s... 相似文献
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基于卷积神经网络的深度学习流场特征识别及应用进展 总被引:1,自引:1,他引:1
深度学习架构的出色性能使得机器学习在流体力学中的应用得到新的发展,可以应对流体力学中诸多问题和需求。卷积神经网络(CNN)强大的非线性映射能力以及分层提取信息特征的功能,使其成为当下流场特征研究不容忽视的工具。围绕这一研究前沿与热点问题,概述和归纳了这一研究领域的进展与成果。首先,对深度学习在流体力学中的发展以及卷积神经网络进行了简单的回顾。然后,从卷积神经网络能够识别特征出发,先后介绍了基于卷积的深度学习特征识别在流场预测、流动外形优化、流场可视化精度提升和生成对抗等应用方面的研究进展。最后,对深度学习在流场识别领域的应用进行了展望,为后续的研究提供参考。 相似文献
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Safety is one of the important topics in the field of civil aviation. Auxiliary Power Unit(APU) is one of important components in aircraft, which provides electrical power and compressed air for aircraft. The hazards in APU are prone to cause economic losses and even casualties. So,actively identifying the hazards in APU before an accident occurs is necessary. In this paper, a Hybrid Deep Neural Network(HDNN) based on multi-time window convolutional neural network-Bidirectional Long Short-Term M... 相似文献
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Drogue detection for autonomous aerial refueling based on convolutional neural networks 总被引:2,自引:0,他引:2
Drogue detection is a fundamental issue during the close docking phase of autonomous aerial refueling(AAR). To cope with this issue, a novel and effective method based on deep learning with convolutional neural networks(CNNs) is proposed. In order to ensure its robustness and wide application, a deep learning dataset of images was prepared by utilizing real data of ‘‘Probe and Drogue" aerial refueling, which contains diverse drogues in various environmental conditions without artificial features placed on the drogues. By employing deep learning ideas and graphics processing units(GPUs), a model for drogue detection using a Caffe deep learning framework with CNNs was designed to ensure the method's accuracy and real-time performance. Experiments were conducted to demonstrate the effectiveness of the proposed method, and results based on real AAR data compare its performance to other methods, validating the accuracy, speed, and robustness of its drogue detection ability. 相似文献
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卷积神经网络和峭度在轴承故障诊断中的应用 总被引:1,自引:1,他引:1
针对传统智能诊断方法依靠专家知识和人工提取数据特征工作量大的问题,结合深度学习方法在特征提取和处理大数据方面的优势,研究了一种基于卷积神经网络和振动信号峭度指标的滚动轴承故障诊断方法。该方法将深度学习应用于轴承故障诊断,提取滚动轴承正常状态、内圈故障、外圈故障和滚动体故障4种状态的振动信号,将振动信号分段处理得到峭度指标,使用数据到图像的转换方法将峭度指标转换为灰度图,送入卷积神经网络模型完成故障分类。在进行滚动轴承故障诊断的实验时,所提的模型诊断准确率达到99.5%,高于传统支持向量机(SVM)算法的95.8%。 相似文献
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Fault diagnosis is vital in manufacturing system.However,the first step of the traditional fault diagnosis method is to process the signal,extract the features and then put the features into a selected classifier for classification.The process of feature extraction depends on the experimenters’experience,and the classification rate of the shallow diagnostic model does not achieve satisfactory results.In view of these problems,this paper proposes a method of converting raw signals into twodimensi... 相似文献