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An Efficient and Robust Fall Detection System Using Wireless Gait Analysis Sensor with Artificial Neural Network (ANN) and Support Vector Machine (SVM) Algorithms 被引量:2
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作者 Bhargava Teja Nukala Naohiro Shibuya +5 位作者 Amanda Rodriguez Jerry Tsay Jerry Lopez Tam Nguyen Steven Zupancic Donald Yu-Chun Lie 《Open Journal of Applied Biosensor》 2014年第4期29-39,共11页
In this work, a total of 322 tests were taken on young volunteers by performing 10 different falls, 6 different Activities of Daily Living (ADL) and 7 Dynamic Gait Index (DGI) tests using a custom-designed Wireless Ga... In this work, a total of 322 tests were taken on young volunteers by performing 10 different falls, 6 different Activities of Daily Living (ADL) and 7 Dynamic Gait Index (DGI) tests using a custom-designed Wireless Gait Analysis Sensor (WGAS). In order to perform automatic fall detection, we used Back Propagation Artificial Neural Network (BP-ANN) and Support Vector Machine (SVM) based on the 6 features extracted from the raw data. The WGAS, which includes a tri-axial accelerometer, 2 gyroscopes, and a MSP430 microcontroller, is worn by the subjects at either T4 (at back) or as a belt-clip in front of the waist during the various tests. The raw data is wirelessly transmitted from the WGAS to a near-by PC for real-time fall classification. The BP ANN is optimized by varying the training, testing and validation data sets and training the network with different learning schemes. SVM is optimized by using three different kernels and selecting the kernel for best classification rate. The overall accuracy of BP ANN is obtained as 98.20% with LM and RPROP training from the T4 data, while from the data taken at the belt, we achieved 98.70% with LM and SCG learning. The overall accuracy using SVM was 98.80% and 98.71% with RBF kernel from the T4 and belt position data, respectively. 展开更多
关键词 Artificial neural network (ANN) Back Propagation FALL Detection FALL Prevention GAIT Analysis SENSOR support vector machine (svm) WIRELESS SENSOR
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基于PSO-LSSVM-BP模型的高边坡力学参数反分析及稳定性评价
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作者 徐卫亚 陈世壮 +5 位作者 张贵科 胡明涛 黄威 许晓逸 张海龙 王如宾 《河海大学学报(自然科学版)》 CAS CSCD 北大核心 2024年第5期52-59,共8页
基于粒子群优化(PSO)算法和最小二乘支持向量机(LSSVM)算法构建非线性映射关系,结合反向传播(BP)神经网络对非线性映射关系生成的数据库进行机器学习,构建了PSO-LSSVM-BP模型确定最优岩体力学参数。PSO-LSSVM-BP模型以高边坡监测位移数... 基于粒子群优化(PSO)算法和最小二乘支持向量机(LSSVM)算法构建非线性映射关系,结合反向传播(BP)神经网络对非线性映射关系生成的数据库进行机器学习,构建了PSO-LSSVM-BP模型确定最优岩体力学参数。PSO-LSSVM-BP模型以高边坡监测位移数据作为输入信息,通过反分析获得高边坡岩体力学参数,将反分析参数用于FLAC3D位移数值计算,结果表明模拟结果与监测数据吻合较好,验证了该模型的可行性和有效性。基于PSO-LSSVM-BP模型,对不同蓄水位下两河口水电站进水口高边坡稳定性进行了评价,发现水位是影响边坡稳定性的主要因素,随着水位上升,边坡位移逐渐增大,其表面和断层处损伤程度加深,边坡局部点安全系数有所下降,但整体点安全系数均大于1.30,有一定安全裕度。 展开更多
关键词 高边坡 力学参数反分析 粒子群优化 最小二乘向量机 反向传播神经网络 两河口水电站
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Flame image recognition of alumina rotary kiln by artificial neural network and support vector machine methods 被引量:18
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作者 张红亮 邹忠 +1 位作者 李劼 陈湘涛 《Journal of Central South University of Technology》 EI 2008年第1期39-43,共5页
Based on the Fourier transform, a new shape descriptor was proposed to represent the flame image. By employing the shape descriptor as the input, the flame image recognition was studied by the methods of the artificia... Based on the Fourier transform, a new shape descriptor was proposed to represent the flame image. By employing the shape descriptor as the input, the flame image recognition was studied by the methods of the artificial neural network(ANN) and the support vector machine(SVM) respectively. And the recognition experiments were carried out by using flame image data sampled from an alumina rotary kiln to evaluate their effectiveness. The results show that the two recognition methods can achieve good results, which verify the effectiveness of the shape descriptor. The highest recognition rate is 88.83% for SVM and 87.38% for ANN, which means that the performance of the SVM is better than that of the ANN. 展开更多
关键词 rotary kiln flame image image recognition shape descriptor artificial neural network support vector machine
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Comparison of School Building Construction Costs Estimation Methods Using Regression Analysis, Neural Network, and Support Vector Machine 被引量:2
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作者 Gwang-Hee Kim Jae-Min Shin +1 位作者 Sangyong Kim Yoonseok Shin 《Journal of Building Construction and Planning Research》 2013年第1期1-7,共7页
Accurate cost estimation at the early stage of a construction project is key factor in a project’s success. But it is difficult to quickly and accurately estimate construction costs at the planning stage, when drawin... Accurate cost estimation at the early stage of a construction project is key factor in a project’s success. But it is difficult to quickly and accurately estimate construction costs at the planning stage, when drawings, documentation and the like are still incomplete. As such, various techniques have been applied to accurately estimate construction costs at an early stage, when project information is limited. While the various techniques have their pros and cons, there has been little effort made to determine the best technique in terms of cost estimating performance. The objective of this research is to compare the accuracy of three estimating techniques (regression analysis (RA), neural network (NN), and support vector machine techniques (SVM)) by performing estimations of construction costs. By comparing the accuracy of these techniques using historical cost data, it was found that NN model showed more accurate estimation results than the RA and SVM models. Consequently, it is determined that NN model is most suitable for estimating the cost of school building projects. 展开更多
关键词 ESTIMATING Construction COSTS Regression Analysis neural network support vector machine
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Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review 被引量:1
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作者 Ernest Yeboah Boateng Joseph Otoo Daniel A. Abaye 《Journal of Data Analysis and Information Processing》 2020年第4期341-357,共17页
In this paper, sixty-eight research articles published between 2000 and 2017 as well as textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN), Support Vector Machines (SVM), Random Forest (... In this paper, sixty-eight research articles published between 2000 and 2017 as well as textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF) and Neural Network (NN) as the main statistical tools were reviewed. The aim was to examine and compare these nonparametric classification methods on the following attributes: robustness to training data, sensitivity to changes, data fitting, stability, ability to handle large data sizes, sensitivity to noise, time invested in parameter tuning, and accuracy. The performances, strengths and shortcomings of each of the algorithms were examined, and finally, a conclusion was arrived at on which one has higher performance. It was evident from the literature reviewed that RF is too sensitive to small changes in the training dataset and is occasionally unstable and tends to overfit in the model. KNN is easy to implement and understand but has a major drawback of becoming significantly slow as the size of the data in use grows, while the ideal value of K for the KNN classifier is difficult to set. SVM and RF are insensitive to noise or overtraining, which shows their ability in dealing with unbalanced data. Larger input datasets will lengthen classification times for NN and KNN more than for SVM and RF. Among these nonparametric classification methods, NN has the potential to become a more widely used classification algorithm, but because of their time-consuming parameter tuning procedure, high level of complexity in computational processing, the numerous types of NN architectures to choose from and the high number of algorithms used for training, most researchers recommend SVM and RF as easier and wieldy used methods which repeatedly achieve results with high accuracies and are often faster to implement. 展开更多
关键词 Classification Algorithms NON-PARAMETRIC K-Nearest-Neighbor neural networks Random Forest support vector machines
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A Comparative Study of Support Vector Machine and Artificial Neural Network for Option Price Prediction 被引量:1
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作者 Biplab Madhu Md. Azizur Rahman +3 位作者 Arnab Mukherjee Md. Zahidul Islam Raju Roy Lasker Ershad Ali 《Journal of Computer and Communications》 2021年第5期78-91,共14页
Option pricing has become one of the quite important parts of the financial market. As the market is always dynamic, it is really difficult to predict the option price accurately. For this reason, various machine lear... Option pricing has become one of the quite important parts of the financial market. As the market is always dynamic, it is really difficult to predict the option price accurately. For this reason, various machine learning techniques have been designed and developed to deal with the problem of predicting the future trend of option price. In this paper, we compare the effectiveness of Support Vector Machine (SVM) and Artificial Neural Network (ANN) models for the prediction of option price. Both models are tested with a benchmark publicly available dataset namely SPY option price-2015 in both testing and training phases. The converted data through Principal Component Analysis (PCA) is used in both models to achieve better prediction accuracy. On the other hand, the entire dataset is partitioned into two groups of training (70%) and test sets (30%) to avoid overfitting problem. The outcomes of the SVM model are compared with those of the ANN model based on the root mean square errors (RMSE). It is demonstrated by the experimental results that the ANN model performs better than the SVM model, and the predicted option prices are in good agreement with the corresponding actual option prices. 展开更多
关键词 machine Learning support vector machine Artificial neural network PREDICTION Option Price
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Structural Reliability Analysis Based on Support Vector Machine and Dual Neural Network Direct Integration Method
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作者 NIE Xiaobo LI Haibin 《Journal of Donghua University(English Edition)》 CAS 2021年第1期51-56,共6页
Aiming at the reliability analysis of small sample data or implicit structural function,a novel structural reliability analysis model based on support vector machine(SVM)and neural network direct integration method(DN... Aiming at the reliability analysis of small sample data or implicit structural function,a novel structural reliability analysis model based on support vector machine(SVM)and neural network direct integration method(DNN)is proposed.Firstly,SVM with good small sample learning ability is used to train small sample data,fit structural performance functions and establish regular integration regions.Secondly,DNN is approximated the integral function to achieve multiple integration in the integration region.Finally,structural reliability was obtained by DNN.Numerical examples are investigated to demonstrate the effectiveness of the present method,which provides a feasible way for the structural reliability analysis. 展开更多
关键词 support vector machine(svm) neural network direct integration method structural reliability small sample data performance function
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A Hybrid Model Based on Back-Propagation Neural Network and Optimized Support Vector Machine with Particle Swarm Algorithm for Assessing Blade Icing on Wind Turbines
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作者 Xiyang Li Bin Cheng +2 位作者 Hui Zhang Xianghan Zhang Zhi Yun 《Energy Engineering》 EI 2021年第6期1869-1886,共18页
With the continuous increase in the proportional use of wind energy across the globe,the reduction of power generation efficiency and safety hazards caused by the icing on wind turbine blades have attracted more consi... With the continuous increase in the proportional use of wind energy across the globe,the reduction of power generation efficiency and safety hazards caused by the icing on wind turbine blades have attracted more consideration for research.Therefore,it is crucial to accurately analyze the thickness of icing on wind turbine blades,which can serve as a basis for formulating corresponding control measures and ensure a safe and stable operation of wind turbines in winter times and/or in high altitude areas.This paper fully utilized the advantages of the support vector machine(SVM)and back-propagation neural network(BPNN),with the incorporation of particle swarm optimization(PSO)algorithms to optimize the parameters of the SVM.The paper proposes a hybrid assessment model of PSO-SVM and BPNN based on dynamic weighting rules.Three sets of icing data under a rotating working state of the wind turbine were used as examples for model verification.Based on a comparative analysis with other models,the results showed that the proposed model has better accuracy and stability in analyzing the icing on wind turbine blades. 展开更多
关键词 support vector machine back propagation neural network particle swarm optimization blade icing assessment
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基于BP和LSSVM的径流分频预测模型构建
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作者 张炳林 李俊 宋松柏 《干旱地区农业研究》 CSCD 北大核心 2024年第3期254-263,275,共11页
针对径流序列具有较强的随机性和波动性特征,提出一种短期月径流预测混合模型CEEMDAN-VMD-(BP,LSSVM)-LSSVM。首先利用自适应白噪声完整集成经验模态分解(CEEMDAN,complete ensemble empirical mode decomposition with adaptive noise... 针对径流序列具有较强的随机性和波动性特征,提出一种短期月径流预测混合模型CEEMDAN-VMD-(BP,LSSVM)-LSSVM。首先利用自适应白噪声完整集成经验模态分解(CEEMDAN,complete ensemble empirical mode decomposition with adaptive noise)将径流序列分解为高频、中频和低频分量,再利用变分模态分解(VMD,variational mode decomposition)方法进一步分解高频分量,并根据样本熵对两次分解得到的子序列进行整合,采用麻雀搜索算法优化的反向传播神经网络(BP,back-propagation neural network)和最小二乘支持向量机(LSSVM,least square support vector machine)分别预测高频分量和中低频分量,最后将不同频率分量训练期的拟合值作为LSSVM的输入,进行二次预测得到最终的径流预测结果。将提出的模型应用于黑河流域莺落峡站和祁连站的月径流预测,验证期相关系数和纳什效率系数均达到0.99以上,对比其他8组对照模型,该模型具有更高的预测精度,可以应用于实际的短期月径流预测。 展开更多
关键词 径流预测 经验模态分解 变分模态分解 样本熵 神经网络 支持向量机
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Support Vector Machine and Artificial Neural Networks for Hydrological Cycles Classifications of a Water Reservoir in the Amazon
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作者 Jean Carlos Arouche Freire Tarcisio da Costa Lobato +3 位作者 Jefferson Magalhaes de Morais Terezinha Ferreira de Oliveira Rachel Anne Hauser-Davis Augusto Cesar Fonseca Saraiva 《通讯和计算机(中英文版)》 2014年第2期111-117,共7页
关键词 支持向量机分类器 人工神经网络 水文循环 分类方法 亚马逊 水库 物理化学参数 计算智能技术
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Fault Identification of Internal Combustion Engine based on Support Vector Machine and Fuzzy Neural Network
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作者 CHEN Decheng HE Xinyu 《International Journal of Plant Engineering and Management》 2022年第3期144-157,共14页
The internal combustion engine is the main power source of current large⁃scale machinery and equipment.Overhaul and maintenance of its faults are important conditions for ensuring the safe and stable operation of mach... The internal combustion engine is the main power source of current large⁃scale machinery and equipment.Overhaul and maintenance of its faults are important conditions for ensuring the safe and stable operation of machinery and equipment,and the identification of faults is a prerequisite.Therefore,the fault identification of internal combustion engines is one of the important directions of current research.In order to further improve the accuracy of the fault recognition of internal combustion engines,this paper takes a certain type of internal combustion engine as the research object,and constructs a support vector machine and a fuzzy neural network fault recognition model.The binary tree multi⁃class classification algorithm is used to determine the priority,and then the fuzzy neural network is verified.The feasibility of the model is proved through experiments,which can quickly identify the failure of the internal combustion engine and improve the failure processing efficiency. 展开更多
关键词 internal combustion engine support vector machine fuzzy neural network fault recognition
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基于SSA-BP-SVM模型的云龙湖水质反演研究
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作者 任中杰 《南京信息工程大学学报》 CAS 北大核心 2024年第2期279-290,共12页
利用遥感技术进行水质监测,全面地掌握水质分布情况对水环境保护具有重要意义.水质参数与地表反射率并非简单的线性关系,BP神经网络和支持向量机(SVM),因其非线性模拟的特点,被广泛应用于水质反演.传统BP神经网络存在收敛缓慢、容易陷... 利用遥感技术进行水质监测,全面地掌握水质分布情况对水环境保护具有重要意义.水质参数与地表反射率并非简单的线性关系,BP神经网络和支持向量机(SVM),因其非线性模拟的特点,被广泛应用于水质反演.传统BP神经网络存在收敛缓慢、容易陷入局部最优的问题;SVM虽然具有很好的拟合能力,但受惩罚系数及核函数参数影响较大.以云龙湖为研究区域,利用Sentinel-2影像和实测数据,针对重要水质参数电导率和浊度,提出一种基于麻雀搜索算法(SSA)优化BP神经网络及SVM的水质反演耦合模型,利用SSA对BP神经网络及SVM进行参数寻优,基于验证集MAE计算模型权重,对SSA-BP、SSA-SVM模型测试组输出层加权计算后获得最终反演结果.与BPNN、SVM、SSA-BP、SSA-SVM模型对比,结果表明:(1)Sentinel-2影像对电导率及浊度的敏感波段均为可见光及短波红外波段;(2)SSA-BP-SVM水质反演耦合模型精度更高,电导率及浊度反演模型R 2分别为0.92、0.89;(3)云龙湖具有典型的城市水体特征,电导率受上游南望净水厂排水影响较大,浊度受社会生产活动带来的颗粒污染物影响较大.基于Sentinel-2影像利用SSA-BP-SVM模型进行水质反演具有较好的应用潜力,能够为云龙湖水质监测以及制定保护措施提供一定的技术支撑. 展开更多
关键词 bp神经网络 支持向量机 麻雀搜索算法 电导率 浊度
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多传感器的BPNN和SVM多源异构数据融合算法
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作者 王晓琪 陈颖聪 +2 位作者 谢敏敏 张嘉慧 蔡上 《计算技术与自动化》 2024年第2期70-76,共7页
多传感器的多源异构数据融合处理时,大量的冗余数据及复杂的非线性可分空间导致能耗较大,为此,提出了BP神经网络和支持向量机的多源异构数据融合算法。以数据关系构建约束条件,利用BP神经网络算法建立数据清洗模型,判定节点变量的活跃程... 多传感器的多源异构数据融合处理时,大量的冗余数据及复杂的非线性可分空间导致能耗较大,为此,提出了BP神经网络和支持向量机的多源异构数据融合算法。以数据关系构建约束条件,利用BP神经网络算法建立数据清洗模型,判定节点变量的活跃程度,优化数据输入;建立数据集合,提取数据特征向量;利用支持向量机泛化能力强、凸优化的特点,获取特征的最优分类超平面,获得非线性可分多源数据集转化为高维线性可分空间的最优决策值,输出结果。实验结果表明,该算法融合多源异构数据的能量消耗小、延迟低,融合效果好。 展开更多
关键词 bp神经网络 支持向量机 多源异构数据 数据清洗 数据融合
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基于BP神经网络和SSA-SVM的接地网腐蚀速率组合预测
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作者 张衡 刘闯 +3 位作者 刘炬 严文帅 刘云飞 陈海旭 《四川电力技术》 2024年第1期59-64,共6页
为提高接地网腐蚀速率预测精度,提出了一种接地网腐蚀速率组合预测方法。首先,采用SSA算法对SVM进行优化,建立接地网SSA-SVM腐蚀预测速率模型;然后,采用6-11-1的BP神经网络对SSA-SVM模型的预测残差进行修正,建立了基于BP神经网络和SSA-... 为提高接地网腐蚀速率预测精度,提出了一种接地网腐蚀速率组合预测方法。首先,采用SSA算法对SVM进行优化,建立接地网SSA-SVM腐蚀预测速率模型;然后,采用6-11-1的BP神经网络对SSA-SVM模型的预测残差进行修正,建立了基于BP神经网络和SSA-SVM的接地网腐蚀速率组合预测模型;最后,采用接地网腐蚀实验数据进行算例分析。结果表明,所提接地网腐蚀速率组合模型预测结果的均方根误差、平均相对误差和相关系数分别为0.192、4.98%和0.974 6,在模型稳定性、预测精度、预测结果与实际值的相关性均优于其他模型,验证了所提模型的正确性和优越性。 展开更多
关键词 接地网 腐蚀速率 组合预测 麻雀搜索算法 支持向量机 bp神经网络
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Machine Learning and Artificial Neural Network for Predicting Heart Failure Risk
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作者 Polin Rahman Ahmed Rifat +3 位作者 MD.IftehadAmjad Chy Mohammad Monirujjaman Khan Mehedi Masud Sultan Aljahdali 《Computer Systems Science & Engineering》 SCIE EI 2023年第1期757-775,共19页
Heart failure is now widely spread throughout the world.Heart disease affects approximately 48%of the population.It is too expensive and also difficult to cure the disease.This research paper represents machine learni... Heart failure is now widely spread throughout the world.Heart disease affects approximately 48%of the population.It is too expensive and also difficult to cure the disease.This research paper represents machine learning models to predict heart failure.The fundamental concept is to compare the correctness of various Machine Learning(ML)algorithms and boost algorithms to improve models’accuracy for prediction.Some supervised algorithms like K-Nearest Neighbor(KNN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF),Logistic Regression(LR)are considered to achieve the best results.Some boosting algorithms like Extreme Gradient Boosting(XGBoost)and Cat-Boost are also used to improve the prediction using Artificial Neural Networks(ANN).This research also focuses on data visualization to identify patterns,trends,and outliers in a massive data set.Python and Scikit-learns are used for ML.Tensor Flow and Keras,along with Python,are used for ANN model train-ing.The DT and RF algorithms achieved the highest accuracy of 95%among the classifiers.Meanwhile,KNN obtained a second height accuracy of 93.33%.XGBoost had a gratified accuracy of 91.67%,SVM,CATBoost,and ANN had an accuracy of 90%,and LR had 88.33%accuracy. 展开更多
关键词 Heart failure prediction data visualization machine learning k-nearest neighbors support vector machine decision tree random forest logistic regression xgboost and catboost artificial neural network
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A Real-Time and Ubiquitous Network Attack Detection Based on Deep Belief Network and Support Vector Machine 被引量:7
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作者 Hao Zhang Yongdan Li +2 位作者 Zhihan Lv Arun Kumar Sangaiah Tao Huang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2020年第3期790-799,共10页
In recent years, network traffic data have become larger and more complex, leading to higher possibilities of network intrusion. Traditional intrusion detection methods face difficulty in processing high-speed network... In recent years, network traffic data have become larger and more complex, leading to higher possibilities of network intrusion. Traditional intrusion detection methods face difficulty in processing high-speed network data and cannot detect currently unknown attacks. Therefore, this paper proposes a network attack detection method combining a flow calculation and deep learning. The method consists of two parts: a real-time detection algorithm based on flow calculations and frequent patterns and a classification algorithm based on the deep belief network and support vector machine(DBN-SVM). Sliding window(SW) stream data processing enables real-time detection, and the DBN-SVM algorithm can improve classification accuracy. Finally, to verify the proposed method, a system is implemented.Based on the CICIDS2017 open source data set, a series of comparative experiments are conducted. The method's real-time detection efficiency is higher than that of traditional machine learning algorithms. The attack classification accuracy is 0.7 percentage points higher than that of a DBN, which is 2 percentage points higher than that of the integrated algorithm boosting and bagging methods. Hence, it is suitable for the real-time detection of high-speed network intrusions. 展开更多
关键词 DEEP BELIEF network(DBN) flow calculation frequent pattern INTRUSION detection SLIDING WINDOW support vector machine(svm)
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Application of Least Square Support Vector Machine (LSSVM) for Determination of Evaporation Losses in Reservoirs 被引量:5
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作者 Pijush Samui 《Engineering(科研)》 2011年第4期431-434,共4页
This article adopts Least Square Support Vector Machine (LSSVM) for prediction of Evaporation Losses (EL) in reservoirs. LSSVM is firmly based on the theory of statistical learning, uses regression technique. The inpu... This article adopts Least Square Support Vector Machine (LSSVM) for prediction of Evaporation Losses (EL) in reservoirs. LSSVM is firmly based on the theory of statistical learning, uses regression technique. The input of LSSVM model is Mean air temperature (T) (?C), Average wind speed (WS)(m/sec), Sunshine hours (SH)(hrs/day), and Mean relative humidity(RH)(%). LSSVM has been used to compute error barn of predicted data. An equation has been developed for the determination of EL. Sensitivity analysis has been also performed to investigate the importance of each of the input parameters. A comparative study has been presented between LSSVM and artificial neural network (ANN) models. This study shows that LSSVM is a powerful tool for determination EL in reservoirs. 展开更多
关键词 EVAPORATION LOSSES Least SQUARE support vector machine Prediction Artificial neural network
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Novel Method of Predicting Network Bandwidth Based on Support Vector Machines
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作者 沈伟 冯瑞 邵惠鹤 《Journal of Beijing Institute of Technology》 EI CAS 2004年第4期454-457,共4页
In order to solve the problems of small sample over-fitting and local minima when neural networks learn online, a novel method of predicting network bandwidth based on support vector machines(SVM) is proposed. The pre... In order to solve the problems of small sample over-fitting and local minima when neural networks learn online, a novel method of predicting network bandwidth based on support vector machines(SVM) is proposed. The prediction and learning online will be completed by the proposed moving window learning algorithm(MWLA). The simulation research is done to validate the proposed method, which is compared with the method based on neural networks. 展开更多
关键词 support vector machines(svm) neural networks network bandwidth bandwidth prediction
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Study of tide prediction method influenced by nonperiodic factors based on support vector machines 被引量:3
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作者 HE Shi-jun ZHOU Wenjun +1 位作者 ZHOU Ruyan HUANG Dongmei 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2012年第5期160-164,共5页
Harmonic analysis, the traditional tidal forecasting method, cannot take into account the impact of noncyclical factors, and is also based on the BP neural network tidal prediction model which is easily limited by the... Harmonic analysis, the traditional tidal forecasting method, cannot take into account the impact of noncyclical factors, and is also based on the BP neural network tidal prediction model which is easily limited by the amount of data. According to the movement of celestial bodies, and considering the insufficient tidal characteristics of historical data which are impacted by the nonperiodic weather, a tidal prediction method is designed based on support vector machine (SVM) to carry out the simulation experiment by using tidal data from Xiamen Tide Gauge, Luchaogang Tide Gauge and Weifang Tide Gauge individually. And the results show that the model satisfactorily carries out the tide prediction which is influenced by noncyclical factors. At the same time, it also proves that the proposed prediction method, which when compared with harmonic analysis method and the BP neural network method, has faster modeling speed, higher prediction precision and stronger generalization ability. 展开更多
关键词 tidal prediction support vector machines celestial motion law harmonic analysis bp neural network nonperiodic factors
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基于改进CNN-SVM的井下钻头磨损状态评估研究
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作者 李玉梅 邓杨林 +3 位作者 李基伟 李乾 杨磊 于丽维 《石油机械》 北大核心 2024年第6期12-19,共8页
现有钻头磨损评估方法中,存在人工特征提取过程可能无法完全提取正确分类所需的信号动态特征,及需要对各个统计量进行大量计算等问题。为此,提出了一种新的基于改进卷积神经网络支持向量机(CNN-SVM)的钻头磨损程度评估算法。该算法将采... 现有钻头磨损评估方法中,存在人工特征提取过程可能无法完全提取正确分类所需的信号动态特征,及需要对各个统计量进行大量计算等问题。为此,提出了一种新的基于改进卷积神经网络支持向量机(CNN-SVM)的钻头磨损程度评估算法。该算法将采集的近钻头原始振动数据导入CNN-Softmax模型,通过训练好的CNN模型从近钻头数据中提取主要的特征参数,将提取的稀疏特征向量输入SVM并进行故障分类,利用遗传算法实现SVM参数的优化选择,最后应用t分布随机邻域法近邻嵌入,使其故障特征学习过程可视化,以评估其特征提取能力。采用该算法对钻头磨损的现场试验数据进行了分析。分析结果表明:基于改进CNN-SVM的井下钻头磨损状态评估算法准确率高达98.33%。所得结论可为实现钻头磨损状态的进一步监测提供理论支撑。 展开更多
关键词 钻头磨损状态评估 卷积神经网络 支持向量机 特征提取可视化 平均池化采样
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