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New Antenna Array Beamforming Techniques Based on Hybrid Convolution/Genetic Algorithm for 5G and Beyond Communications
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作者 Shimaa M.Amer Ashraf A.M.Khalaf +3 位作者 Amr H.Hussein Salman A.Alqahtani Mostafa H.Dahshan Hossam M.Kassem 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第3期2749-2767,共19页
Side lobe level reduction(SLL)of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service(QOS)in recent and future wireless communication systems starting from 5G up t... Side lobe level reduction(SLL)of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service(QOS)in recent and future wireless communication systems starting from 5G up to 7G.Furthermore,it improves the array gain and directivity,increasing the detection range and angular resolution of radar systems.This study proposes two highly efficient SLL reduction techniques.These techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm(GA)to develop the Conv/GA andDConv/GA,respectively.The convolution process determines the element’s excitations while the GA optimizes the element spacing.For M elements linear antenna array(LAA),the convolution of the excitation coefficients vector by itself provides a new vector of excitations of length N=(2M−1).This new vector is divided into three different sets of excitations including the odd excitations,even excitations,and middle excitations of lengths M,M−1,andM,respectively.When the same element spacing as the original LAA is used,it is noticed that the odd and even excitations provide a much lower SLL than that of the LAA but with amuch wider half-power beamwidth(HPBW).While the middle excitations give the same HPBWas the original LAA with a relatively higher SLL.Tomitigate the increased HPBWof the odd and even excitations,the element spacing is optimized using the GA.Thereby,the synthesized arrays have the same HPBW as the original LAA with a two-fold reduction in the SLL.Furthermore,for extreme SLL reduction,the DConv/GA is introduced.In this technique,the same procedure of the aforementioned Conv/GA technique is performed on the resultant even and odd excitation vectors.It provides a relatively wider HPBWthan the original LAA with about quad-fold reduction in the SLL. 展开更多
关键词 Array synthesis convolution process genetic algorithm(ga) half power beamwidth(HPBW) linear antenna array(LAA) side lobe level(SLL) quality of service(QOS)
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基于SPA-GA-SVR模型的土壤水分及温度预测 被引量:5
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作者 朱成杰 汪正权 《中国农村水利水电》 北大核心 2024年第1期30-36,共7页
土壤湿度和温度是影响水文循环和气候变化的重要参数,在农业实践活动和生态平衡中起着重要作用。为及时、准确地监测土壤含水量(Soil Moisture Content,SMC)及温度,提出了一种基于高光谱数据的预测方法。实验数据集来自为期5天的实地测... 土壤湿度和温度是影响水文循环和气候变化的重要参数,在农业实践活动和生态平衡中起着重要作用。为及时、准确地监测土壤含水量(Soil Moisture Content,SMC)及温度,提出了一种基于高光谱数据的预测方法。实验数据集来自为期5天的实地测量,所获得的高光谱数据包含大量的噪声及冗余信息,因此首先用Savitzky-Golay卷积平滑对光谱数据进行降噪处理,利用连续投影算法(Successive Projection Algorithm,SPA)提取数据特征波长,然后通过遗传算法(Genetic Algorithm,GA)对支持向量机回归(Support Vector Regression,SVR)的超参数权值和偏置进行优化,构建SPA-GASVR混合算法模型对土壤水分和温度进行预测,并与BP神经网络(Back Propagation Neural Network,BPNN)、SPA-BP、SVR、SPA-SVR、GA-SVR这5种模型的预测性能进行比较。实验结果表明:各模型在土壤湿度低于30%的情况下,表现出的预测能力差异并不显著。但整体上,复合模型相比于单一的神经网络或机器学习模型具有明显的优势,且经过连续投影算法优化的模型进一步的提高其预测能力,最终SPA-GA-SVR算法在各项指标上均优于其他模型,土壤水分预测模型的R^(2)=0.981、RMSE=0.473%,土壤温度预测模型R^(2)=0.963、RMSE=0.883℃。实验证明基于高光谱数据,经过SPA和GA优化的SVR模型能实现对土壤湿度和温度精准的预测。该方法具有一定的应用价值和现实意义,可应用于便携式高光谱仪和无人机上,实现对土壤水分和温度的实时监测,为今后的播种及灌溉提供理论参考。 展开更多
关键词 土壤水分 土壤温度 高光谱 连续投影算法(SPA) 遗传算法-支持向量机回归(ga-SVR)
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基于GA-LSTM自适应卡尔曼滤波的路面不平度识别 被引量:1
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作者 李韶华 李健玮 冯桂珍 《振动与冲击》 EI CSCD 北大核心 2024年第9期121-130,共10页
准确、快速地识别出车辆当前行驶的路面激励信息,是实现智能底盘控制进而保证车辆平顺性的关键。针对传统路面不平度识别算法准确率低、自适应性差等问题,提出了基于遗传算法(genetic algorithm,GA)优化长短期记忆神经网络(long short-t... 准确、快速地识别出车辆当前行驶的路面激励信息,是实现智能底盘控制进而保证车辆平顺性的关键。针对传统路面不平度识别算法准确率低、自适应性差等问题,提出了基于遗传算法(genetic algorithm,GA)优化长短期记忆神经网络(long short-term memory networks,LSTM)自适应卡尔曼滤波的路面不平度识别算法。基于2自由度车辆悬架模型,通过灰色关联法选择LSTM神经网络的特征输入变量,并采用GA优化LSTM神经网络的模型参数以准确识别路面等级,并据此实时更新卡尔曼滤波器算法中的噪声矩阵,实现了在复杂路况下对路面不平度的自适应识别。仿真和试验研究表明,所提出的基于GA-LSTM自适应卡尔曼滤波算法能够快速准确的识别路面不平度与路面等级,与传统卡尔曼滤波算法相比,相关系数、均方根误差和最大绝对误差分别提高3.11%、37.5%和51.2%,表明所提算法对复杂工况具有很好的自适应能力。 展开更多
关键词 路面不平度识别 自适应卡尔曼滤波器 ga-LSTM 灰色关联法
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基于GA的RBF神经网络气液两相流持液率预测模型优化
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作者 廖锐全 李龙威 +2 位作者 王伟 马斌 潘元 《长江大学学报(自然科学版)》 2024年第2期91-100,共10页
为了提高气液两相流持液率预测精度,针对传统径向基函数(RBF)神经网络预测气液两相流持液率网络拓扑结构困难和收敛速度慢等问题,提出一种基于遗传算法(GA)优化径向基函数神经网络的气液两相流持液率预测模型。通过系统聚类算法和灰色... 为了提高气液两相流持液率预测精度,针对传统径向基函数(RBF)神经网络预测气液两相流持液率网络拓扑结构困难和收敛速度慢等问题,提出一种基于遗传算法(GA)优化径向基函数神经网络的气液两相流持液率预测模型。通过系统聚类算法和灰色关联度分析(GRA)对收集的实验数据进行处理,优选出最优模型特征,同时结合遗传算法确定了RBF神经网络结构参数。基于室内实验数据进行训练,并与常用于持液率预测的反向传播(BP)神经网络、GA-BP神经网络及RBF神经网络进行对比,评估了模型的准确性及可行性。结果表明:GA-RBF神经网络模型均方误差为0.0017,均方根误差为0.0416,平均绝对误差为0.0281,拟合度为0.9483。相较于其他神经网络模型,该预测模型表现出更高的计算精度和更强的泛化能力。 展开更多
关键词 持液率 气液两相流 RBF神经网络 遗传算法 数据清洗
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基于GA-BP神经网络的大型客机气流角估计方法
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作者 张伟 张喆 +1 位作者 龚孝懿 王昕楠 《计算机仿真》 2024年第1期53-57,102,共6页
为了解决硬件冗余难以克服的气流角传感器共因故障问题,进一步提高飞机气流角信号的可靠性,研究了基于GABP神经网络的气流角估计方法。通过BP神经网络融合姿态角、加速度、风速等参数来实现不依赖气流角传感器的气流角估计;引入遗传算... 为了解决硬件冗余难以克服的气流角传感器共因故障问题,进一步提高飞机气流角信号的可靠性,研究了基于GABP神经网络的气流角估计方法。通过BP神经网络融合姿态角、加速度、风速等参数来实现不依赖气流角传感器的气流角估计;引入遗传算法对神经网络权值和阈值进行全局优化,提高估计精度;对某大型客机的试飞数据预处理后用于模型的训练和测试。仿真结果表明,训练完成的GA-BP神经网络模型对气流角的估计值贴近实际值,稳定性和精度明显高于BP神经网络。上述方法给飞机增加一个余度的气流角信号,可用于传感器故障时为飞机提供可靠的气流角信号。 展开更多
关键词 气流角估计 神经网络 遗传算法 试飞数据预处理 大型客机
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基于GA-BP神经网络的新疆南疆核桃树生长模型研究
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作者 陈杰 《无线互联科技》 2024年第4期16-18,22,共4页
文章提出了一种利用遗传算法优化BP神经网络的核桃树生长模型来预测核桃树的树高、胸径的方法,通过优化BP神经网络的权值和阈值建立GA-BP模型,与多元线性回归模型的预测结果进行比较。结果表明:采用遗传算法优化后的模型具有更高的预测... 文章提出了一种利用遗传算法优化BP神经网络的核桃树生长模型来预测核桃树的树高、胸径的方法,通过优化BP神经网络的权值和阈值建立GA-BP模型,与多元线性回归模型的预测结果进行比较。结果表明:采用遗传算法优化后的模型具有更高的预测精度,对核桃树生长预测具有指导意义。 展开更多
关键词 遗传算法 DB神经网络 ga-BP模型 核桃树生长模型
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Parameters selection in gene selection using Gaussian kernel support vector machines by genetic algorithm 被引量:11
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作者 毛勇 周晓波 +2 位作者 皮道映 孙优贤 WONG Stephen T.C. 《Journal of Zhejiang University-Science B(Biomedicine & Biotechnology)》 SCIE EI CAS CSCD 2005年第10期961-973,共13页
In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying result... In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying results by using conventional linear sta- tistical methods. Recursive feature elimination based on support vector machine (SVM RFE) is an effective algorithm for gene selection and cancer classification, which are integrated into a consistent framework. In this paper, we propose a new method to select parameters of the aforementioned algorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice of selecting the apparently best parameters by using a genetic algorithm to search for a couple of optimal parameter. Fast implementation issues for this method are also discussed for pragmatic reasons. The proposed method was tested on two repre- sentative hereditary breast cancer and acute leukaemia datasets. The experimental results indicate that the proposed method per- forms well in selecting genes and achieves high classification accuracies with these genes. 展开更多
关键词 Gene selection Support VECTOR machine (SVM) RECURSIVE feature ELIMINATION (RFE) genetic algorithm (ga) Parameter SELECTION
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基于GRA-GASA-SVM的煤层瓦斯含量预测方法研究 被引量:2
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作者 田水承 任治鹏 马磊 《煤炭技术》 CAS 2024年第1期114-118,共5页
为提升煤层瓦斯含量预测精度,提出一种采用遗传模拟退火算法混合优化支持向量机(SVM)参数的瓦斯含量预测模型(GRA-GASA-SVM模型)。该模型将GA和SA整合为遗传模拟退火算法协同优化SVM的参数,以解决传统网格寻优算法取值范围无法确定和单... 为提升煤层瓦斯含量预测精度,提出一种采用遗传模拟退火算法混合优化支持向量机(SVM)参数的瓦斯含量预测模型(GRA-GASA-SVM模型)。该模型将GA和SA整合为遗传模拟退火算法协同优化SVM的参数,以解决传统网格寻优算法取值范围无法确定和单一智能算法优化程度有限等问题。利用灰色关联分析(GRA)压缩数据集维度,建立瓦斯含量预测参数体系并作为GASA-SVM的输入数据集。结果表明:SVM模型、GA-SVM模型和GASA-SVM模型10折交叉验证瓦斯含量预测总平均相对误差分别为15.98%、13.55%和10.58%。相比SVM模型和GA-SVM模型,GASA-SVM模型预测稳定性更优、预测精准度更高且对新样本泛化能力更强。 展开更多
关键词 遗传算法(ga) 模拟退火算法(SA) 支持向量机(SVM) 煤层瓦斯含量 灰色关联分析(GRA)
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FPGA PLACEMENT OPTIMIZATION BY TWO-STEP UNIFIED GENETIC ALGORITHM AND SIMULATED ANNEALING ALGORITHM 被引量:6
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作者 Yang Meng A.E.A. Almaini Wang Pengjun 《Journal of Electronics(China)》 2006年第4期632-636,共5页
Genetic Algorithm (GA) is a biologically inspired technique and widely used to solve numerous combinational optimization problems. It works on a population of individuals, not just one single solution. As a result, it... Genetic Algorithm (GA) is a biologically inspired technique and widely used to solve numerous combinational optimization problems. It works on a population of individuals, not just one single solution. As a result, it avoids converging to the local optimum. However, it takes too much CPU time in the late process of GA. On the other hand, in the late process Simulated Annealing (SA) converges faster than GA but it is easily trapped to local optimum. In this letter, a useful method that unifies GA and SA is introduced, which utilizes the advantage of the global search ability of GA and fast convergence of SA. The experimental results show that the proposed algorithm outperforms GA in terms of CPU time without degradation of performance. It also achieves highly comparable placement cost compared to the state-of-the-art results obtained by Versatile Place and Route (VPR) Tool. 展开更多
关键词 genetic Algorithm (ga Simulated Annealing (SA) PLACEMENT FPga EDA
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基于GA-PSO算法的冻土本构模型参数识别 被引量:1
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作者 梁靖宇 张跃东 路德春 《冰川冻土》 CSCD 2024年第1期235-246,共12页
遗传算法(GA)与粒子群算法(PSO)分别具有缺乏目标导向性和易陷入局部最优的缺点,但同时分别具有全局搜索能力强与能有效传递优势信息的优点。本文以GA计算步结合精英保留策略作为PSO计算步的优势信息,避免PSO算法陷入局部最优,以PSO计... 遗传算法(GA)与粒子群算法(PSO)分别具有缺乏目标导向性和易陷入局部最优的缺点,但同时分别具有全局搜索能力强与能有效传递优势信息的优点。本文以GA计算步结合精英保留策略作为PSO计算步的优势信息,避免PSO算法陷入局部最优,以PSO计算步结合非精英优化策略作为GA计算步的导向信息,克服GA算法缺乏目标导向的问题,建立了GA-PSO新算法。其具体过程为,通过采用GA计算步对解空间进行全局搜索并对精英个体进行保留,进一步,将适应度较差的个体利用PSO计算步进行优化。基于多峰函数的验证结果表明,GA-PSO算法在解空间中具有更强的全局搜索能力,同时具有更快的收敛速度。将GA-PSO算法应用到冻土非正交弹塑性本构模型的参数识别中,通过模型的参数识别以及模型预测结果对比与验证,结果表明GA-PSO算法能够有效识别冻土非正交弹塑性本构模型的参数,提升了模型的预测效果。 展开更多
关键词 参数识别 冻土本构模型 优化算法 遗传算法(ga) 粒子群算法(PSO)
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Geochemical Characteristics and Genetic Types of Natural Gas in the Xinchang Gas Field, Sichuan Basin, SW China 被引量:11
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作者 WU Xiaoqi LIU Quanyou +5 位作者 LIU Guangxiang WANG Ping LI Huaji MENG Qingqiang CHEN Yingbin ZENG Huasheng 《Acta Geologica Sinica(English Edition)》 SCIE CAS CSCD 2017年第6期2200-2213,共14页
The molecular compositions and stable carbon and hydrogen isotopic compositions of natural gas from the Xinchang gas field in the Sichuan Basin were investigated to determine the genetic types. The natural gas is main... The molecular compositions and stable carbon and hydrogen isotopic compositions of natural gas from the Xinchang gas field in the Sichuan Basin were investigated to determine the genetic types. The natural gas is mainly composed of methane (88.99%-98.01%), and the dryness coefficient varies between 0.908 and 0.997. The gas generally displays positive alkane carbon and hydrogen isotopic series. The geochemical characteristics and gas-source correlation indicate that the gases stored in the 5th member of the Upper Triassic Xujiahe Formation are coal-type gases which are derived from source rocks in the stratum itself. The gases reservoired in the 4th member of the Xujiahe Formation and Jurassic strata in the Xinchang gas field are also coal-type gases that are derived from source rocks in the 3rd and 4th members of the Xujiahe Formation. The gases reservoired in the 2nd member of the Upper Triassic Xujiahe Formation are mainly coal-type gases with small amounts of oil-type gas that is derived from source rocks in the stratum itself. This is accompanied by a small amount of contribution brought by source rocks in the Upper Triassic Ma'antang and Xiaotangzi formations. The gases reservoired in the 4th member of the Middle Triassic Leikoupo Formation are oil-type gases and are believed to be derived from the secondary cracking of oil which is most likely to be generated from the Upper Permian source rocks. 展开更多
关键词 natural gas geochemical characteristics genetic types Xinchang gas field western Sichuan Basin
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基于PSO-GA的分片区块链系统性能优化方法
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作者 蒋腾聪 张建山 +1 位作者 郑鸿强 陈星 《小型微型计算机系统》 CSCD 北大核心 2024年第7期1756-1762,共7页
在这篇文章中,针对分片区块链(Sharded Blockchain)系统性能优化问题,提出了一种结合粒子群和遗传算法的系统性能优化方法(PSO-GA),目的是为了在尽可能满足当前网络环境情况下,提升其系统吞吐量.该方法考虑分片区块链中节点的计算能力... 在这篇文章中,针对分片区块链(Sharded Blockchain)系统性能优化问题,提出了一种结合粒子群和遗传算法的系统性能优化方法(PSO-GA),目的是为了在尽可能满足当前网络环境情况下,提升其系统吞吐量.该方法考虑分片区块链中节点的计算能力、恶意节点的概率以及节点之间的传输速率等不同网络环境下,找到响应网络状态的最佳分片区块链系统参数;为了避免传统粒子群优化算法陷入局部最优的问题,引入遗传算法中的交叉操作和变异操作,有效提高方法的准确性.通过大量仿真实验对方法的有效性进行验证分析.实验结果表明,相比于其他的方法,本文所提出的方法可以在更短的时间取得更高的系统吞吐量. 展开更多
关键词 分片区块链 可扩展性 粒子群算法 遗传算法
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Design of artificial neural networks using a genetic algorithm to predict saturates of vacuum gas oil 被引量:15
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作者 Dong Xiucheng Wang Shouchun +1 位作者 Sun Renjin Zhao Suoqi 《Petroleum Science》 SCIE CAS CSCD 2010年第1期118-122,共5页
Accurate prediction of chemical composition of vacuum gas oil (VGO) is essential for the routine operation of refineries. In this work, a new approach for auto-design of artificial neural networks (ANN) based on a... Accurate prediction of chemical composition of vacuum gas oil (VGO) is essential for the routine operation of refineries. In this work, a new approach for auto-design of artificial neural networks (ANN) based on a genetic algorithm (GA) is developed for predicting VGO saturates. The number of neurons in the hidden layer, the momentum and the learning rates are determined by using the genetic algorithm. The inputs for the artificial neural networks model are five physical properties, namely, average boiling point, density, molecular weight, viscosity and refractive index. It is verified that the genetic algorithm could find the optimal structural parameters and training parameters of ANN. In addition, an artificial neural networks model based on a genetic algorithm was tested and the results indicated that the VGO saturates can be efficiently predicted. Compared with conventional artificial neural networks models, this approach can improve the prediction accuracy. 展开更多
关键词 Saturates vacuum gas oil PREDICTION artificial neural networks genetic algorithm
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Genetic types and distribution of shallow-buried natural gases 被引量:5
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作者 Gao Yang Jin Qiang Zhu Guangyou 《Petroleum Science》 SCIE CAS CSCD 2010年第3期347-354,共8页
Great volumes of shallow-buried (〈2,000 m) natural gases which are mainly composed of biogases and low-mature gases have been found in the Mesozoic-Cenozoic sedimentary basins in China. Many shallow gas reservoirs ... Great volumes of shallow-buried (〈2,000 m) natural gases which are mainly composed of biogases and low-mature gases have been found in the Mesozoic-Cenozoic sedimentary basins in China. Many shallow gas reservoirs in China are characterized by coexistence of biogas and low-mature gas, so identifying the genetic types of shallow gases is important for exploration and development in sedimentary basins. In this paper, we study the gas geochemistry characteristics and distribution in different basins, and classify the shallow gas into two genetic types, biogas and low-mature gas. The biogases are subdivided further into two subtypes by their sources, the source rock-derived biogas and hydrocarbon-derived biogas. Based on the burial history of the source rocks, the source rock-derived biogases are divided into primary and secondary biogas. The former is generated from the source rocks in the primary burial stage, and the latter is from uplifted source rocks or those in a secondary burial stage. In addition, the identifying parameters of each type of shallow gas are given. Based on the analysis above, the distributions of each type of shallow gas are studied. The primary biogases generated from source rocks are mostly distributed in Quaternary basins or modem deltas. Most of them migrate in watersoluble or diffused mode, and their migration distance is short. Reservoir and caprock assemblages play an important role in primary biogas accumulation. The secondary biogases are distributed in a basin with secondary burial history. The oil-degraded biogases are distributed near heavy oil pools. The low-mature gases are widely distributed in shallow-buried reservoirs in the Meso-Cenozoic basins. 展开更多
关键词 Primary biogas secondary biogas oil-degraded biogas low-mature gas genetic type gas identification gas accumulation DISTRIBUTION
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A New Image Watermarking Scheme Using Genetic Algorithm and Residual Numbers with Discrete Wavelet Transform
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作者 Peter Awonnatemi Agbedemnab Mohammed Akolgo Moses Apambila Agebure 《Journal of Information Security》 2023年第4期422-436,共15页
Transmission of data over the internet has become a critical issue as a result of the advancement in technology, since it is possible for pirates to steal the intellectual property of content owners. This paper presen... Transmission of data over the internet has become a critical issue as a result of the advancement in technology, since it is possible for pirates to steal the intellectual property of content owners. This paper presents a new digital watermarking scheme that combines some operators of the Genetic Algorithm (GA) and the Residue Number (RN) System (RNS) to perform encryption on an image, which is embedded into a cover image for the purposes of watermarking. Thus, an image watermarking scheme uses an encrypted image. The secret image is embedded in decomposed frames of the cover image achieved by applying a three-level Discrete Wavelet Transform (DWT). This is to ensure that the secret information is not exposed even when there is a successful attack on the cover information. Content creators can prove ownership of the multimedia content by unveiling the secret information in a court of law. The proposed scheme was tested with sample data using MATLAB2022 and the results of the simulation show a great deal of imperceptibility and robustness as compared to similar existing schemes. 展开更多
关键词 Discrete Wavelet Transform (DWT) Digital Watermarking ENCRYPTION genetic Algorithm (ga) Residue Number System (RNS) gaRN
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基于GA-GRNN的AWJ强化3D打印AlSi10Mg表面性能实验研究
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作者 张苗苗 侯荣国 +3 位作者 吕哲 王龙庆 石广行 王中庆 《现代制造工程》 CSCD 北大核心 2024年第7期35-41,共7页
为提高磨料水射流(Abrasive Water Jet,AWJ)强化工艺对3D打印AlSi10Mg表面性能的强化效果预测的准确性及高效性,首先开展磨料水射流强化AlSi10Mg表面强化实验;然后分别以表面硬度和表面残余应力作为目标,基于遗传算法-广义回归神经网络(... 为提高磨料水射流(Abrasive Water Jet,AWJ)强化工艺对3D打印AlSi10Mg表面性能的强化效果预测的准确性及高效性,首先开展磨料水射流强化AlSi10Mg表面强化实验;然后分别以表面硬度和表面残余应力作为目标,基于遗传算法-广义回归神经网络(Genetic Algorithm-Generalized Ragression Neural Network,GA-GRNN)对实验数据样本进行训练,建立3D打印AlSi10Mg表面性能预测模型;最后,利用遗传算法对建立的神经网络预测模型中的AWJ强化主要参数进行优化。研究结果表明,经过磨料水射流强化后的AlSi10Mg表面硬度与表面残余应力均得到有效提高;建立的GA-GRNN预测模型与校验值误差在2.3%以内,具有较高的准确性;经遗传算法优化后,得到表面硬度最佳参数组合:射流压力为33 MPa,磨料粒径为0.15 mm,靶距为12.4 mm,此时表面硬度为159.25HV;表面残余应力最佳参数组合:射流压力为40 MPa,磨料粒径为0.13 mm,靶距为15 mm,此时表面残余应力为-137.4 MPa。为后续磨料水射流强化零件表面的参数选择提供数据支撑。 展开更多
关键词 磨料水射流 3D打印的AlSi10Mg 表面强化 ga-GRNN神经网络 遗传算法
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The Genetic Mechanism and Model of Deep-Basin Gas Accumulation and Methods for Predicting the Favorable Areas 被引量:2
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作者 WANGTao PANGXiongqi +2 位作者 MAXinhua JINZhijun JIANGZhenxue 《Acta Geologica Sinica(English Edition)》 SCIE CAS CSCD 2003年第4期547-556,共10页
As a kind of abnormal natural gas formed with special mechanism, the deep-basin gas, accumulated in the lower parts of a basin or syncline and trapped by a tight reservoir, has such characteristics as gas-water invers... As a kind of abnormal natural gas formed with special mechanism, the deep-basin gas, accumulated in the lower parts of a basin or syncline and trapped by a tight reservoir, has such characteristics as gas-water inversion, abnormal pressure, continuous distribution and tremendous reserves. Being a geological product of the evolution of petroliferous basins by the end of the middle-late stages, the formation of a deep-basin gas accumulation must meet four conditions, i.e., continuous and sufficient gas supply, tight reservoirs in continuous distribution, good sealing caps and stable structures. The areas, where the expansion force of natural gas is smaller than the sum of the capillary force and the hydrostatic pressure within tight reservoirs, are favorable for forming deep-basin gas pools. The range delineated by the above two forces corresponds to that of the deep-basin gas trap. Within the scope of the deep-basin gas trap, the balance relationship between the amounts of ingoing and overflowing gases determines the gas-bearing area of the deep-basin gas pool. The gas volume in regions with high porosity and high permeability is worth exploring under current technical conditions and it is equivalent to the practical resources (about 10%-20% of the deep-basin gas). Based on studies of deep-basin gas formation conditions, the theory of force balance and the equation of material balance, the favorable areas and gas-containing ranges, as well as possible gas-rich regions are preliminarily predicted in the deep-basin gas pools in the Upper Paleozoic He-8 segment of the Ordos basin. 展开更多
关键词 deep-basin gas geology of natural gas genetic mechanism Ordos basin China
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基于GA-BP神经网络模型的石墨烯发声器研究
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作者 胡卜元 王德波 《传感技术学报》 CAS CSCD 北大核心 2024年第10期1764-1769,共6页
为了解决传统石墨烯发声器物理模型计算量大、精度不高的问题,提出了一种基于GA遗传算法的BP神经网络模型,该算法模型具有更高的精度和适应性。首先,介绍了石墨烯热声发声原理以及实验设计,建立了GA-BP神经网络模型。其次,对模型的参数... 为了解决传统石墨烯发声器物理模型计算量大、精度不高的问题,提出了一种基于GA遗传算法的BP神经网络模型,该算法模型具有更高的精度和适应性。首先,介绍了石墨烯热声发声原理以及实验设计,建立了GA-BP神经网络模型。其次,对模型的参数进行了调节,并对比了基于dropout、基于正则化、基于正则化和GA遗传算法的三种神经网络模型。随后在模型中输入石墨烯发声器的正弦激励幅值、频率以及测量距离,使用GA遗传算法对隐藏层的权值和偏置进行全局寻优,将寻优结果代入BP神经网络,最终预测出给定条件下的声压级。结果表明,在均使用正则化的条件下,BP神经网络预测准确度为98.05%,均方差为0.23;GA-BP神经网络预测准确度达到98.62%,均方差仅为0.14。优化后精准度提高了0.57%,均方差降低了41.36%,展现出更加优异的准确性和适应性。该研究为预测多类特征传感器的非线性输出结果提供了一种高精准度、高适应性的方案。 展开更多
关键词 石墨烯热声发声器 BP神经网络 ga遗传算法 声压级
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基于GA-BP神经网络战储仓库选址决策模型
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作者 李晓星 陈玲 +1 位作者 南旭东 戴剑华 《兵工自动化》 北大核心 2024年第7期74-78,共5页
针对各战略方向战储仓库选址决策问题,提出基于GA-BP神经网络的战储仓库选址决策模型。通过分析战储仓库选址的影响因素,构建战储库选址决策指标体系;基于BP神经网络建立选址决策模型,运用遗传算法(geneticalgorithm,GA)对反向传播(back... 针对各战略方向战储仓库选址决策问题,提出基于GA-BP神经网络的战储仓库选址决策模型。通过分析战储仓库选址的影响因素,构建战储库选址决策指标体系;基于BP神经网络建立选址决策模型,运用遗传算法(geneticalgorithm,GA)对反向传播(backpropagation,BP)神经网络的权值和阈值进行优化,并给出实例证明遗传算法优化BP神经网络,可提高选址决策的效率和精准度。结果表明,该研究可为科学开展战储仓库选址工作提供思路和方法。 展开更多
关键词 遗传算法 ga-BP神经网络 战储仓库 选址决策
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基于GA⁃BP神经网络的氢气传感器的浓度补偿研究
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作者 王雅坤 张宝林 +3 位作者 王兆成 周传君 郭仕佳 马琬雲 《传感器与微系统》 CSCD 北大核心 2024年第11期33-36,共4页
为解决环境因素致使氢气浓度传感器测量精度误差较大,导致氢燃料电池车辆因氢气泄漏检测不精确而产生爆炸风险的问题,提出了一种基于遗传算法反向传播(GA⁃BP)神经网络的氢气浓度传感器的浓度补偿方法。首先,利用BP神经网络对氢气浓度进... 为解决环境因素致使氢气浓度传感器测量精度误差较大,导致氢燃料电池车辆因氢气泄漏检测不精确而产生爆炸风险的问题,提出了一种基于遗传算法反向传播(GA⁃BP)神经网络的氢气浓度传感器的浓度补偿方法。首先,利用BP神经网络对氢气浓度进行初步预测;然后,通过GA在寻优方面的优势进行浓度补偿,解决了BP神经网络局部陷入极值的问题。实验结果表明:基于GA⁃BP神经网络的氢气浓度传感器的浓度补偿方法对热导型氢气浓度传感器的预测准确度达到99.98%,最大相对误差值为0.2%,氢气浓度传感器的测量准确度提高了50.8%,为氢燃料汽车行业的发展奠定了基础。 展开更多
关键词 氢气浓度传感器 反向传播神经网络 遗传算法 遗传算法—反向传播神经网络
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