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Modeling of mechanical properties of as-cast Mg-Li-Al alloys based on PSO-BP algorithm 认领 引用 被引量：1
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作者 Li Ming Hao Hai +3 位作者 Zhang Aimin Song Yingde Liu Zhao Zhang Xingguo 《China Foundry》 SCIE CAS 2012年第2期119-124,共6页
Artificial neural networks have been widely used to predict the mechanical properties of alloys in material research. This study aims to investigate the implicit relationship between the compositions and mechanical pr... Artificial neural networks have been widely used to predict the mechanical properties of alloys in material research. This study aims to investigate the implicit relationship between the compositions and mechanical properties of as-cast Mg-Li-AI alloys. Based on the experimental collection of the tensile strength and the elongation of representative Mg-Li-AI alloys, a momentum back-propagation （BP） neural network with a single hidden layer was established. Particle swarm optimization （PSO） was applied to optimize the BP model. In the neural network, the input variables were the contents of Mg, Li and AI, and the output variables were the tensile strength and the elongation. The results show that the proposed PSO-BP model can describe the quantitative relationship between the Mg-Li-AI alloy＇s composition and its mechanical properties. It is possible that the mechanical properties to be predicted without experiment by inputting the alloy composition into the trained network model. The prediction of the influence of AI addition on the mechanical properties of as-cast Mg-Li-AI alloys is consistent with the related research results. 展开更多
关键词 artificial neural networks Mg-Li-Al alloys BP algorithm particle swarm optimization mechanical properties
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A serialized civil aircraft R&D cost estimation model considering commonality based on BP algorithm 认领 引用 被引量：2
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作者 Yongjie ZHANG Kang CAO +2 位作者 Ke LIANG Yongqi ZENG Wenjun DONG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2022年第4期253-265,共13页
The common design of serial civil aircraft, an important strategy of modern civil aircraft research and develop-ment, minimizes the whole life cycle cost of civil aircraft through asset reuse and resource sharing. How... The common design of serial civil aircraft, an important strategy of modern civil aircraft research and develop-ment, minimizes the whole life cycle cost of civil aircraft through asset reuse and resource sharing. However, the existing estimating model for the R&D cost of civil aircraft ignores the effects of common design, so the value estimated by estimating derivative models is significantly inconsistent with the actual one. To solve this problem, a novel assessment method for civil aircraft commonality indicators is developed based on fuzzy set in the present study, exploiting the attributes and structural parameters of the aircraft to be assessed as input to determine the degree of membership that pertains to the commonality sub-interval as the commonality indicator.Then the BP(Back Propagation) neural network algorithm is adopted to establish the relationship between the common index and the decrease rate of the R&D cost of derivative models. The model employs over a dozen typical civil aircraft models(e.g., Boeing, Airbus, and Bombardier) as the sample data for network learning training to build a mature neural network model for estimating the R&D cost of novel derivative models. As revealed from the comparative analysis on the calculated results of the samples, the estimated results of the model given the effects of commonality in the present study exhibit higher estimation accuracy and value for future work. 展开更多
关键词 BP algorithm Civil aircraft R&D cost Common indicators Fuzzy set Serialized civil aircraft
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An Improved BP Algorithm and Its Application in Classification of Surface Defects of Steel Plate 认领 引用 被引量：4
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作者 ZHAO Xiang-yang LAI Kang-sheng DAI Dong-ming 《Journal of Iron and Steel Research International》 SCIE CAS 2007年第2期52-55,共4页
Artificial neural network is a new approach to pattern recognition and classification. The model of multilayer perceptron （MLP） and back-propagation （BP） is used to train the algorithm in the artificial neural net... Artificial neural network is a new approach to pattern recognition and classification. The model of multilayer perceptron （MLP） and back-propagation （BP） is used to train the algorithm in the artificial neural network. An improved fast algorithm of the BP network was presented, which adopts a singular value decomposition （SVD） and a generalized inverse matrix. It not only increases the speed of network learning but also achieves a satisfying precision. The simulation and experiment results show the effect of improvement of BP algorithm on the classification of the surface defects of steel plate. 展开更多
关键词 artificial neural network MLP BP algorithm SVD generalized inverse matrix
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Salt and Pepper Noise Filter Based on GA-BP Algorithm Noise Detector 认领 引用 被引量：2
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作者 宋寅卯 李晓娟 《光电工程》 CAS CSCD 北大核心 2011年第2期59-64,共6页
基于噪声检测的中值滤波器已广泛用于消除图像中的椒盐噪声，然而在高噪声密度情况下，对噪声像素的定位不准确很容易造成图像边缘的模糊。本文提出了一种基于GA-BP的椒盐噪声滤波算法，克服了这一缺陷。算法首先用遗传算法优化的BP网... 基于噪声检测的中值滤波器已广泛用于消除图像中的椒盐噪声，然而在高噪声密度情况下，对噪声像素的定位不准确很容易造成图像边缘的模糊。本文提出了一种基于GA-BP的椒盐噪声滤波算法，克服了这一缺陷。算法首先用遗传算法优化的BP网络对图像中的噪声像素定位，然后引入保边函数和PRP算法求目标函数的极值进而实现图像的去噪处理。实验结果表明，该算法比传统滤波算法效果有明显改善，且具有良好的泛化性、鲁棒性和自适应性。 展开更多
关键词 GA-BP算法 椒盐噪声 噪声检测 保边函数 PRP算法
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Research on a Fog Computing Architecture and BP Algorithm Application for Medical Big Data 认领 引用
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作者 Baoling Qin 《Intelligent Automation & Soft Computing》 SCIE 2023年第7期255-267,共13页
Although the Internet of Things has been widely applied,the problems of cloud computing in the application of digital smart medical Big Data collection,processing,analysis,and storage remain,especially the low efficie... Although the Internet of Things has been widely applied,the problems of cloud computing in the application of digital smart medical Big Data collection,processing,analysis,and storage remain,especially the low efficiency of medical diagnosis.And with the wide application of the Internet of Things and Big Data in the medical field,medical Big Data is increasing in geometric magnitude resulting in cloud service overload,insufficient storage,communication delay,and network congestion.In order to solve these medical and network problems,a medical big-data-oriented fog computing architec-ture and BP algorithm application are proposed,and its structural advantages and characteristics are studied.This architecture enables the medical Big Data generated by medical edge devices and the existing data in the cloud service center to calculate,compare and analyze the fog node through the Internet of Things.The diagnosis results are designed to reduce the business processing delay and improve the diagnosis effect.Considering the weak computing of each edge device,the artificial intelligence BP neural network algorithm is used in the core computing model of the medical diagnosis system to improve the system computing power,enhance the medical intelligence-aided decision-making,and improve the clinical diagnosis and treatment efficiency.In the application process,combined with the characteristics of medical Big Data technology,through fog architecture design and Big Data technology integration,we could research the processing and analysis of heterogeneous data of the medical diagnosis system in the context of the Internet of Things.The results are promising:The medical platform network is smooth,the data storage space is sufficient,the data processing and analysis speed is fast,the diagnosis effect is remarkable,and it is a good assistant to doctors’treatment effect.It not only effectively solves the problem of low clinical diagnosis,treatment efficiency and quality,but also reduces the waiting time of patients,effectively solves the contradiction between doctors and patients,and improves the medical service quality and management level. 展开更多
关键词 Medical big data IoT fog computing distributed computing BP algorithm model
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Nonlinear Inversion for Complex Resistivity Method Based on QPSO-BP Algorithm 认领 引用 被引量：1
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作者 Weixin Zhang Jinsuo Liu +1 位作者 Le Yu Biao Jin 《Open Journal of Geology》 CAS 2021年第10期494-508,共15页
The significant advantage of the complex resistivity method is to reflect the abnormal body through multi-parameters, but its inversion parameters are more than the resistivity tomography method. Therefore, how to eff... The significant advantage of the complex resistivity method is to reflect the abnormal body through multi-parameters, but its inversion parameters are more than the resistivity tomography method. Therefore, how to effectively invert these spectral parameters has become the focused area of the complex resistivity inversion. An optimized BP neural network (BPNN) approach based on Quantum Particle Swarm Optimization (QPSO) algorithm was presented, which was able to improve global search ability for complex resistivity multi-parameter nonlinear inversion. In the proposed method, the nonlinear weight adjustment strategy and mutation operator were used to enhance the optimization ability of QPSO algorithm. Implementation of proposed QPSO-BPNN was given, the network had 56 hidden neurons in two hidden layers (the first hidden layer has 46 neurons and the second hidden layer has 10 neurons) and it was trained on 48 datasets and tested on another 5 synthetic datasets. The training and test results show that BP neural network optimized by the QPSO algorithm performs better than the BP neural network without initial optimization on the inversion training and test models, and the mean square error distribution is better. At the same time, a double polarized anomalous bodies model was also used to verify the feasibility and effectiveness of the proposed method, the inversion results show that the QPSO-BP algorithm inversion clearly characterizes the anomalous boundaries and is closer to the values of the parameters. 展开更多
关键词 Complex Resistivity Finite Element Method Nonlinear Inversion QPSO-BP Algorithm 2.5D Numerical Simulation
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Motion Control of Underwater Vehicle Based on Least Disturbance BP Algorithm 认领 引用 被引量：3
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作者 LIU Xue-min, LIU Jian-cheng, XU Yu-ruCollege of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001 , China 《Journal of Marine Science and Application》 2002年第1期16-20,共5页
Up to now, some technology of neural networks are developed to solve the non-linearity of researched objects and to implement the adaptive control in many engineering fields, and some good results were achieved. Thoug... Up to now, some technology of neural networks are developed to solve the non-linearity of researched objects and to implement the adaptive control in many engineering fields, and some good results were achieved. Though it puts some questions over to design application structure with neural networks, it is really unknowable about the study mechanism of those. But, the importance of study ratio is widely realized by many scientists now, and some methods on the modification of that are provided. The main subject is how to improve the stability and how to increase the convergent rate of networks by defining a good form of the study ratio. Here a new algorithm named LDBP (least disturbance BP algorithm) is proposed to calculate the ratio online according to the output errors, the weights of network and the input values. The algorithm is applied to the control of an autonomous underwater vehicle designed by HEU. The experimental results show that the algorithm has good performance and the controller designed based on it is fine. 展开更多
关键词 BP algorithm of neural networks dynamic ratio least disturbance autonomous underwater vehicle
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Circle BP Algorithm for MLP Neural Network 认领 引用 被引量：1
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作者 CHEN Jianyong,CHEN Zhenxiang,LU Yingyang,XU Shenchu 《Semiconductor Photonics and Technology》 1998年第3期179-182,192,共4页
A simple new BP algorithm named circle BP algorithm is introduced.With this algorithm,local minimums can be completely got rid of and learning speed can improve dramatically.It can be easily designed into the circuitr... A simple new BP algorithm named circle BP algorithm is introduced.With this algorithm,local minimums can be completely got rid of and learning speed can improve dramatically.It can be easily designed into the circuitry and advance further the application of MLP neural network . 展开更多
关键词 Circle BP Algorithm Neural Network XOR Network
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The tool for building an NN based on improved BP algorithm 认领 引用
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作者 冯玉强 潘启澍 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2001年第3期312-316,共5页
Back propagation (BP) algorithm is a very useful algorithm in many areas, but its leaning process is a very complicated non linear convergence process, in which, chaos often happens, and slow convergence speed and loc... Back propagation (BP) algorithm is a very useful algorithm in many areas, but its leaning process is a very complicated non linear convergence process, in which, chaos often happens, and slow convergence speed and local least often make it difficult for the non experts to use it widely, and an improved BP (IBP) algorithm is therefore suggested to expedite the convergence speed. The algorithm can judge local least and take some steps automatically to jump out from the local least. Furthermore, this algorithm introduces the expert knowledge base. An IBP based agile and current neural network (NN) constructed tool is designed. An initial NN can be constructed automatically using an expert knowledge base. And an Aitken’s Δ 2 process method is used to expedite the convergent speed for NN. Besides, the method of changing the parameter of Sigmoid function and increasing the hidden node is used to bring surge for NN to jump out from the local 展开更多
关键词 neural network (NN) BP algorithm
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Demarcation of potential seismic sources on integration of genetic algorithm and BP algorithm 认领 引用
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作者 ZHOU Qing +1 位作者 YE Hong 《Acta Seismologica Sinica(English Edition)》 2002年第6期677-682,共6页
In this paper potential seismic sources in coastal region of South China are identified by integration of genetic algorithm (GA) and back propagation (BP algorithm). GA is used for finding the best parameter combinati... In this paper potential seismic sources in coastal region of South China are identified by integration of genetic algorithm (GA) and back propagation (BP algorithm). GA is used for finding the best parameter combination rapidly in an infinite solution space for artificial neural networks (ANN). The results show that the distribution of potential seismic sources with different upper magnitude demarcated by this classifier is mostly satisfied the intrinsic relationship between seismic environment and earthquake occurrence, with less effect from subjective judgment of human being. 展开更多
关键词 genetic algorithm BP algorithm potential seismic sources
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基于SSA-BP和ICEEMDAN-NTEO算法的电缆故障识别及精确定位方法 认领 引用 被引量：2
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作者 袁发庭 李昊樾 +4 位作者 李胡强 杨毅 简盛开 江宇晴 唐波 《电力科学与技术学报》 CAS CSCD 北大核心 2026年第2期127-144,共18页
现有电力电缆行波测距方法依赖初始行波的准确识别,存在故障定位不准确的问题。对此,基于电磁暂态仿真软件ATP-EMTP,建立10 kV电力电缆输电线路模型,提出基于麻雀搜索算法优化的BP神经网络和改进的Teager能量算子(novel teager energy o... 现有电力电缆行波测距方法依赖初始行波的准确识别,存在故障定位不准确的问题。对此,基于电磁暂态仿真软件ATP-EMTP,建立10 kV电力电缆输电线路模型,提出基于麻雀搜索算法优化的BP神经网络和改进的Teager能量算子(novel teager energy operator,NTEO)的双端行波定位方法。首先,建立电力电缆传输线路模型,研究不同工况下故障电流波形,利用基于麻雀搜索算法优化的反向传播神经网络(sparrow search algorithm-back propagation neural network,SSA-BP)算法识别电缆故障类型,训练集与测试集预测结果表明SSA-BP算法能够准确、快速辨识电力电缆故障类型。其次,通过对电缆三相电流进行相模变换,根据电力电缆不同故障类型选择合适的故障分量对电缆进行故障定位。再次,采用改进自适应噪声完备集合经验模态分解(improved complete ensemble empirical mode decomposition with adaptive noise,ICEEMDAN)算法对故障波形进行分解,滤除故障信号中的噪音干扰,通过NTEO算法增强初始行波波头特征,精确定位初始行波到达检测器的时间,实现电力电缆故障的精确定位。最后,采用仿真分析,对方法进行验证。研究结果表明:在考虑不同短路故障、接地电阻和故障距离等因素影响下,其故障识别精度达到98.3%;而CEEMD-NTEO和小波变换算法的故障定位精度分别为99.83%和99.67%,所提方法定位精度为99.88%。该研究成果为电缆故障准确识别和定位提供了重要理论依据。 展开更多
关键词 配电线路 双端行波定位 SSA-BP算法 NTEO算法 ICEEMDAN算法 ATP-EMTP
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基于CS-BP-PID算法的烟叶密集烤房温度控制系统 认领 引用 被引量：1
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作者 沈少君 闫九福 +4 位作者 卢雨 林晓路 杜超凡 朱荣光 孟令峰 《农机化研究》 北大核心 2026年第4期95-102,共8页
烟叶烘烤作为决定烟叶品质的核心环节,其温湿度控制的精准性至关重要。针对当前密集烤房多阶段温度控制精度差、波动范围大、响应时间长等直接影响烟叶色泽、香气、化学成分、经济价值等问题,设计了一种基于布谷鸟算法(CS)优化的BP神经... 烟叶烘烤作为决定烟叶品质的核心环节,其温湿度控制的精准性至关重要。针对当前密集烤房多阶段温度控制精度差、波动范围大、响应时间长等直接影响烟叶色泽、香气、化学成分、经济价值等问题,设计了一种基于布谷鸟算法(CS)优化的BP神经网络PID控制器。通过模拟布谷鸟的寄生行为和莱维飞行特性,对BP神经网络的初始权重进行优化,加快了BP神经网络的自学习速度,以实现密集烤房温度的快速精准调控,降低了超调量,提高了响应速度。同时,基于树莓派4B搭建了密集烤房温湿度控制试验平台,并对控制器性能进行了验证。结果表明:CS-BP-PID控制器上升时间为79.35 s,峰值时间为180.00 s,调节时间为249.38 s,最大超调量为3.25%,相比常规PID控制器缩短了38.18%,调节时间缩短了47.05%,峰值时间和最大超调量减少了50%以上,满足系统温度控制需求。通过多阶段烟叶烘烤试验,上等烟比例提高了14.45%,经济效益得到了显著提升。该控制器综合性能优良,达到了精准控温控湿的效果。 展开更多
关键词 烟叶密集烤房 温度控制系统 CS-BP-PID算法
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基于GA-BP神经网络的碳纤维复合芯导线压接缺陷识别方法 认领 引用 被引量：1
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作者 杜志叶 黄子韧 +2 位作者 俸波 岳国华 廖永力 《电工技术学报》 EI CSCD 北大核心 2026年第1期315-328,共14页
碳纤维复合芯导线因其低碳节能等特性,在输电线路的增容改造中有着良好的应用前景。但碳纤维芯棒十分脆弱,技术工艺不成熟,由于压接不良导致的断线事故时有发生,制约了该技术的推广应用。为此,该文针对断裂和少压两种严重压接缺陷,提出... 碳纤维复合芯导线因其低碳节能等特性,在输电线路的增容改造中有着良好的应用前景。但碳纤维芯棒十分脆弱,技术工艺不成熟,由于压接不良导致的断线事故时有发生,制约了该技术的推广应用。为此,该文针对断裂和少压两种严重压接缺陷,提出一种碳纤维复合芯导线压接缺陷的漏磁检测信号缺陷特征提取方法。通过实验优化,以漏磁检测信号数据中7个峰值点的幅值、21个相对位置信息和7个波形类型信息作为缺陷判断特征值,有效地提高了缺陷种类和缺陷程度识别的准确度。对碳纤维芯导线进行磁性制备,并研制相对应的漏磁检测装置,生产106根不同类型、不同程度的碳纤维芯压接缺陷样品,得到613组漏磁检测信号数据并完成特征值提取,搭建基于遗传算法(GA)的反向传播(BP)神经网络。实测数据表明,该方法可以有效地完成对碳纤维复合芯导线压接缺陷类型的识别,同时对缺陷程度的识别准确率可达到94.31%。 展开更多
关键词 碳纤维复合芯导线 缺陷识别 磁性制备 漏磁检测 遗传算法 BP神经网络
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基于PSO-BP算法的平-摆筛参数交互对分层效果的影响 认领 引用
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作者 牛福生 张红梅 +2 位作者 张晋霞 王研 于晓东 《矿产综合利用》 CAS 2026年第1期151-160,共10页
以平-摆双运行模式直线振动筛参数:振幅、振频、振动方向角和摆动角度为研究对象,探究不同参数交互作用对分层效果的影响。【目的】为更加直观地分析振动筛的筛分参数与分层效果的复杂影响关系以及筛分过程中料体群的动态特性规律,便于... 以平-摆双运行模式直线振动筛参数:振幅、振频、振动方向角和摆动角度为研究对象,探究不同参数交互作用对分层效果的影响。【目的】为更加直观地分析振动筛的筛分参数与分层效果的复杂影响关系以及筛分过程中料体群的动态特性规律,便于开展振动筛参数优化研究,提升料体筛分分层效果及筛分效率。【方法】首先使用solid works建立振动筛简化三维模型,使用正交实验法设计实验方案,并将实验方案中对应因素水平导入EDEM中,以神经网络为载体探究振动筛的振幅、振动频率、振动方向角和摆动角度四个参数与分层效果的变化规律,对获得的数据结果进行深度学习。【结果】将筛分参数与不同分层效果进行影响权重分析,发现振动频率、振动幅度、振动方向角和摆动角度的影响效果最为显著,故以这四种参数组合表征振动筛运行状态,以明晰不同参数对料体分层效果的变化规律。当振幅为3.4 mm,振频为14.8 Hz,振动方向角为44.1°,摆动角度为0.6°时,料体群分层效果明显,细粒级物料总体分布于筛面端。【结论】本文以筛机振动参数为变量和料体分层效果为优化目标,以期为振动筛优化设计提供启发。 展开更多
关键词 振动筛 振动参数 离散元 分层沉降比 PSO-BP算法
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基于BP算法的复合域大规模S盒FPGA优化与实现 认领 引用
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作者 张磊 代景晨 +3 位作者 洪睿鹏 肖超恩 李国元 王建新 《现代电子技术》 北大核心 2026年第13期96-104,共9页
为解决MK-3算法大规模S盒硬件实现资源占用量大、同构函数组合数爆炸的问题,文中提出一种基于XOR数量最少的S盒硬件实现优化方法。首先,通过多项式基的复合域GF(((24)2)2)将S盒优化方法转换为同构矩阵与同构逆矩阵二元矩阵乘XO... 为解决MK-3算法大规模S盒硬件实现资源占用量大、同构函数组合数爆炸的问题,文中提出一种基于XOR数量最少的S盒硬件实现优化方法。首先,通过多项式基的复合域GF(((24)2)2)将S盒优化方法转换为同构矩阵与同构逆矩阵二元矩阵乘XOR数量最小问题;其次,基于BP算法筛选得到最优同构矩阵和同构逆矩阵;最后,采用Vivado开发环境进行FPGA实现。实验结果表明,基于BP算法优化的S盒FPGA实现时钟频率LUT达到了0.41621,与已有的方案相比,该方法在降低硬件资源消耗的同时提高了时钟频率,取得了较好的优化效果。 展开更多
关键词 分组密码算法 S盒 BP算法 MK-3算法 同构函数 有限域 多项式基 FPGA
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基于IWOA-BP的红松人工林枯落针叶层火蔓延速率预测模型 认领 引用 被引量：1
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作者 黄天棋 辛颖 张敏 《南京林业大学学报（自然科学版）》 CAS CSCD 北大核心 2026年第2期29-36,共8页
【目的】红松(Pinus koraiensis)针叶油脂含量较高,存在极高的森林火灾风险,地表火蔓延是其主要的火灾传播方式。本研究通过构建地表火蔓延速率预测模型,为红松人工林的火灾防控提供科学依据。【方法】以黑龙江省凉水地区红松人工林枯... 【目的】红松(Pinus koraiensis)针叶油脂含量较高,存在极高的森林火灾风险,地表火蔓延是其主要的火灾传播方式。本研究通过构建地表火蔓延速率预测模型,为红松人工林的火灾防控提供科学依据。【方法】以黑龙江省凉水地区红松人工林枯落针叶层为材料,进行松针含水率为0、5%、10%、15%、20%,坡度为0、5°、10°、15°,风速为0、1、2、3、4、5 m/s的360组室内点烧试验,根据热电偶法测定火蔓延速率,构建改进鲸鱼优化算法(IWOA)-BP神经网络模型对火蔓延速率进行预测,并与3种模型(WOA-BP神经网络、GA-BP神经网络和PSO-BP神经网络)进行预测结果对比。【结果】坡度、风速与火蔓延速度均呈极显著正相关(P<0.01),含水率与火蔓延速度呈显著负相关(P<0.05);火蔓延速率随可燃物含水率的增加而降低,随风速和坡度的增加而升高,在风速为4 m/s时,火蔓延增长速率达到最大值。IWOA算法引入Tent混沌映射、改进非线性收敛因子、增加自适应权重和Levy飞行运动,增加了算法的随机性和多样性,提高了收敛速度,同时避免陷入局部最优,具备较高预测精度和鲁棒性;IWOA优化的BP神经网络模型精度和稳定性明显高于其他3种模型,对实测数据的模型适应度最佳。【结论】IWOA-BP神经网络模型能有效地预测红松人工林枯落针叶层的火蔓延速率,为林火防控与森林地表凋落物的火蔓延速率预测模型研究提供科学指导。 展开更多
关键词 红松人工林 火蔓延速率 点烧试验 改进鲸鱼优化算法(IWOA)算法 BP神经网络
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基于HHO-BP神经网络的混合动力重型拖拉机机电耦合系统故障诊断 认领 引用 被引量：1
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作者 邓晓亭 宋思雨 +1 位作者 鲁植雄 朱烨均 《农业工程学报》 EI CAS CSCD 北大核心 2026年第7期37-47,共11页
混合动力拖拉机的故障诊断对保障设备安全运行具有重要工程意义。混联式混合动力重型拖拉机中混合动力耦合箱对整个传动系统的安全稳定性起到重要作用,该研究提出一种耦合箱的机械故障诊断方法。针对混合动力耦合箱的5种常见故障类型进... 混合动力拖拉机的故障诊断对保障设备安全运行具有重要工程意义。混联式混合动力重型拖拉机中混合动力耦合箱对整个传动系统的安全稳定性起到重要作用,该研究提出一种耦合箱的机械故障诊断方法。针对混合动力耦合箱的5种常见故障类型进行分析,并基于这些故障类型开展算法研究。传统故障诊断方法在提取振动信号特征时常因敏感度不够而难以区分微弱故障信号,同时,基于BP(back propagation)神经网络的模型易陷入局部最优,影响诊断精度与效率。针对上述问题,本文首次将哈里斯鹰优化(harris hawks optimization,HHO)算法引入BP网络参数寻优,并结合时频域特征设计了一种HHO-BP神经网络故障诊断方法。通过加速度振动传感器采集箱体的振动信号,对420组原始信号数据进行处理,提取时频域故障特征。分别构建BP神经网络、PSO-BP(particle swarm optimization-back propagation)神经网络和HHO-BP神经网络故障诊断模型,并进行对比分析。精确率、召回率和F1分数等评价指标的对比结果表明,HHO-BP模型的整体性能优于其他两种模型。在分类平均准确率方面,HHO-BP模型达到98.26%,分别比BP和PSOBP模型提高了6.36和5.54个百分点。综合对比结果表明,HHO-BP优化算法在混合动力耦合箱机械故障诊断中表现出良好的稳定性和判断能力,可为解决混合动力重型拖拉机的机电耦合系统机械故障问题提供有效途径。 展开更多
关键词 拖拉机 故障诊断 混合动力 混合动力耦合箱 BP神经网络 HHO优化算法
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基于IFA-BP神经网络模型的变电站碳排放预测 认领 引用
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作者 王巍 李智威 +5 位作者 张赵阳 张洪 周蠡 王振 黄放 王灿 《广西师范大学学报（自然科学版）》 CAS 北大核心 2026年第2期103-114,共12页
针对现有变电站碳排放量预测模型存在考虑指标较少、数据更新慢等问题,本文提出一种基于改进萤火虫算法(improved firefly algorithm,IFA)优化反向传播(back propagation,BP)神经网络的变电站碳排放预测模型。首先,针对萤火虫算法(firef... 针对现有变电站碳排放量预测模型存在考虑指标较少、数据更新慢等问题,本文提出一种基于改进萤火虫算法(improved firefly algorithm,IFA)优化反向传播(back propagation,BP)神经网络的变电站碳排放预测模型。首先,针对萤火虫算法(firefly algorithm,FA)收敛速度过慢以及易陷入局部最优等问题,引入教与学因子,修改萤火虫位置更新过程,以提高群体适应度。其次,引入IFA算法对BP神经网络模型进行超参数寻优,并构建IFA-BP神经网络预测模型。然后,基于CRITIC法筛选预测模型输入层的关键碳排放指标。最后,利用训练集数据训练预测模型,基于训练好的模型对变电站的碳排放量进行预测。仿真结果表明,相较于3种对比方案,本文IFA-BP神经网络预测模型分别在均方根误差(root mean square error,RMSE)上降低59.61%、15.77%和26.65%,在决定系数(coefficient of determination,R2)上提高5.66%、1.46%和1.15%,充分验证了本文所提变电站碳排放预测模型的可行性与优越性。 展开更多
关键词 碳排放 变电站 改进萤火虫算法 BP神经网络 教与学因子
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基于GA优化BP神经网络预测开关柜内部设备温度 认领 引用
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作者 桑仲庆 袁会生 +4 位作者 游一民 戴冬云 周顺雄 廖权昌 姜维云 《高压电器》 CAS CSCD 北大核心 2026年第8期34-42,共9页
由于开关柜通入电流后,内部设备会产生热量,当温度长期超出阈值,会造成设备损坏无法保证安全,因此需要对开关柜内部温度进行监测,提前对柜内设备的温度进行预测,方便对设备进行维护。为了能够准确预测开关柜内部设备温度,基于BP神经网络... 由于开关柜通入电流后,内部设备会产生热量,当温度长期超出阈值,会造成设备损坏无法保证安全,因此需要对开关柜内部温度进行监测,提前对柜内设备的温度进行预测,方便对设备进行维护。为了能够准确预测开关柜内部设备温度,基于BP神经网络,采用GA算法对BP神经网络优化,提出GA-BP神经网络开关柜内部设备温度预测模型。首先分析影响开关柜温度上升的影响因素,并将其作为预测模型的输入数据;再通过预测模型的训练与测试;最后通过衡量指标来评价网络模型的优劣。测试结果表明,该方法能够有效预测开关柜内部设备的温度值,为变电站内设备进行维护提供了便利。 展开更多
关键词 开关柜 预测 温度 BP神经网络 GA算法
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基于MPC的永磁同步电机GWO-BP神经网络控制 认领 引用
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作者 张金亮 任钊月 +1 位作者 简炜 王志虎 《机械设计与制造》 北大核心 2026年第5期92-98,共7页
针对永磁同步电机单矢量模型预测控制在一个周期内只有一个基本电压矢量作用于逆变器,从而造成电流和转矩波动较大,尽管双矢量模型预测控制在一定程度能够改善该问题,但其电压矢量通过遍历法选择,存在计算量大、实时性差等问题,因此提... 针对永磁同步电机单矢量模型预测控制在一个周期内只有一个基本电压矢量作用于逆变器,从而造成电流和转矩波动较大,尽管双矢量模型预测控制在一定程度能够改善该问题,但其电压矢量通过遍历法选择,存在计算量大、实时性差等问题,因此提出了一种基于灰狼算法(Grey Wolf Optimizer,GWO)优化BP(Back Propagation,BP)神经网络的永磁同步电机控制方法。将原系统选择最优电压矢量过程看作是一种神经网络分类任务,通过原系统产生的大量离散数据离线训练网络,并利用GWO算法优化BP神经网络的初始权值和偏置,加快神经网络的训练速度和精度,训练好的网络代替模型预测控制,避免矢量遍历选择。最后仿真验证了该控制策略的可行性,有效的减小了电流和转矩的波动,提高了系统控制性能。 展开更多
关键词 永磁同步电机 模型预测控制 双矢量 灰狼算法 BP神经网络
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