An External Ballistics Fitting Method Based on the Support Vector Machine

2013 ◽  
Vol 753-755 ◽  
pp. 2855-2858 ◽  
Author(s):  
Zhong Liu ◽  
Jing Xin An ◽  
Guo Dong Zhang
Keyword(s):  
Adaptive Learning ◽  
Degrees Of Freedom ◽  
Particle Trajectory ◽  
Small Sample ◽  
Learning Ability ◽  
Support Vector ◽  
Good Precision ◽  
Ballistic Coefficient ◽  
Fitting Method ◽  
Actual Trajectory

It is because of many reasons the trajectory calculated from the theoretical model and the actual trajectory have some error, so the experimental results on the theoretical trajectory must be corrected. In this paper, two degrees of freedom of particle trajectory equations are used to determine the ballistic coefficient. And a SVM Neural Network which has a great learning ability and generalization ability of the extremely small sample is used to adaptive learning the solver deviation of the fit between the trajectory and measured trajectory and amend the ballistic coefficient and modified theoretical trajectory solver results. The test shows that this method has a good precision and stability, and the algorithm can be simple programmed. And it has some value in engineering.

A Research of GPS Height Fitting in Mountainous Terrain by CPSO Optimization FLS-SVM

2013 ◽  
Vol 336-338 ◽  
pp. 2339-2343 ◽  
Author(s):  
Yong Gan ◽  
Xin Xin Liu ◽  
Yuan Pan Zheng
Keyword(s):  
Support Vector Machine ◽  
Small Sample ◽  
Least Square ◽  
Support Vector ◽  
Mountainous Area ◽  
Machine Model ◽  
Normal Height ◽  
Fitting Method ◽  
Fitting In ◽  
The Impact

For the problem of data limited in the mountainous area, a method of FLS-SVM (Fuzzy Least Square Vector Machine) that supporting small sample data and having high noise ability was put forward. The CPSO(chaos particle swarm optimization algorithm) is adopted to optimize the parameters of least squares support vector machine algorithm, and to avoid the uncertainty of artificial parameter selection. Meanwhile, considering the impact of terrain, the terrain correction is introduced to the support vector machine model. The experimental results show that the model can get higher precision fitting effect compared with traditional fitting method such as PSO-LSSVM and GA-LSSVM, and suitable for the SRTM application of getting normal height.

scholarly journals Fuzzy reliability analysis based on support vector machine

2021 ◽  
Author(s):  
xiao bo Nie ◽  
Haibin Li ◽  
Hongxia Chen ◽  
Ruying Pang ◽  
Honghua Sun
Keyword(s):  
Support Vector Machine ◽  
Probability Distribution ◽  
Reliability Analysis ◽  
Small Sample ◽  
Learning Ability ◽  
Support Vector ◽  
Fuzzy Reliability ◽  
Performance Function ◽  
Sample Data ◽  
Distribution Parameters

Abstract For a structure with implicit performance function structure and less sample data, it is difficult to obtain accurate probability distribution parameters by traditional statistical analysis methods. To address the issue, the probability distribution parameters of samples are often regarded as fuzzy numbers. In this paper, a novel fuzzy reliability analysis method based on support vector machine is proposed. Firstly, the fuzzy variable is converted into an equivalent random variable, and the equivalent mean and equivalent standard deviation are calculated. Secondly, the support vector regression machine with excellent small sample learning ability is used to train the sample data. Subsequently, and the performance function is approximated. Finally, the Monte Carlo method is used to obtain fuzzy reliability. Numerical examples are investigated to demonstrate the effectiveness of the proposed method, which provides a feasible way for fuzzy reliability analysis problems of small sample data.

Research Status of Fault Diagnosis Based on Support Vector Machine

2013 ◽  
Vol 475-476 ◽  
pp. 787-791
Author(s):  
Li Mei Liu ◽  
Jian Wen Wang ◽  
Ying Guo ◽  
Hong Sheng Lin
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Small Sample ◽  
Learning Ability ◽  
Practical Significance ◽  
Support Vector ◽  
Limited Information ◽  
Risk Minimization ◽  
Minimization Principle ◽  
Good Learning

Support vector machine has good learning ability and it is good to perform the structural risk minimization principle of statistical learning theory and its application in fault diagnosis of the biggest advantages is that it is suitable for small sample decision. Its nature of learning method is under the condition of limited information to maximize the implicit knowledge of classification in data mining and it is of great practical significance for fault diagnosis. This paper analyzed and summarized the present situation of application of support vector machine in fault diagnosis and made a meaningful exploration on development direction of the future.

Application of the Least Squares Support Vector Machine for Life Prediction of Vital Parts

2014 ◽  
Vol 584-586 ◽  
pp. 2129-2132
Author(s):  
Hong Kai Wang ◽  
Ji Sheng Ma ◽  
Li Qing Fang ◽  
Da Lin Wu ◽  
Yan Feng Yang
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Curve Fitting ◽  
Life Prediction ◽  
Large Scale ◽  
Development Trend ◽  
Small Sample ◽  
Support Vector ◽  
Fitting Method ◽  
Curve Fitting Method

In order to better study the wear state of vital parts of the large scale equipment, and overcoming the disadvantage of small sample of vital parts, we use the least squares support vector machine (LS_SVM) algorithm to predict the wear state of vital parts. Using of quantum particle swarm optimization (QPSO) to optimize parameters least squares support vector machine, and achieved good results. Compared those with the method that use of curve fitting to predict the data development trend, show that this method is superior to the curve fitting method, and has good application value.

scholarly journals Influence of hinge length and distribution number on the camber and cornering properties of a non-pneumatic tire

2020 ◽  
Vol 12 (12) ◽  
pp. 168781402098468
Author(s):  
Xianbin Du ◽  
Youqun Zhao ◽  
Yijiang Ma ◽  
Hongxun Fu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Adaptive Learning ◽  
High Speed ◽  
Back Propagation ◽  
Lateral Force ◽  
Learning Ability ◽  
Vehicle Performance ◽  
Neural Network Theory ◽  
Bp Neural Network Model

The camber and cornering properties of the tire directly affect the handling stability of vehicles, especially in emergencies such as high-speed cornering and obstacle avoidance. The structural and load-bearing mode of non-pneumatic mechanical elastic (ME) wheel determine that the mechanical properties of ME wheel will change when different combinations of hinge length and distribution number are adopted. The camber and cornering properties of ME wheel with different hinge lengths and distributions were studied by combining finite element method (FEM) with neural network theory. A ME wheel back propagation (BP) neural network model was established, and the additional momentum method and adaptive learning rate method were utilized to improve BP algorithm. The learning ability and generalization ability of the network model were verified by comparing the output values with the actual input values. The camber and cornering properties of ME wheel were analyzed when the hinge length and distribution changed. The results showed the variation of lateral force and aligning torque of different wheel structures under the combined conditions, and also provided guidance for the matching of wheel and vehicle performance.

scholarly journals G-computation and machine learning for estimating the causal effects of binary exposure statuses on binary outcomes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Florent Le Borgne ◽  
Arthur Chatton ◽  
Maxime Léger ◽  
Rémi Lenain ◽  
Yohann Foucher
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Statistical Power ◽  
Small Sample ◽  
Causal Effects ◽  
Small Samples ◽  
Support Vector ◽  
Sample Sizes ◽  
Super Learner ◽  
Small Sample Sizes

AbstractIn clinical research, there is a growing interest in the use of propensity score-based methods to estimate causal effects. G-computation is an alternative because of its high statistical power. Machine learning is also increasingly used because of its possible robustness to model misspecification. In this paper, we aimed to propose an approach that combines machine learning and G-computation when both the outcome and the exposure status are binary and is able to deal with small samples. We evaluated the performances of several methods, including penalized logistic regressions, a neural network, a support vector machine, boosted classification and regression trees, and a super learner through simulations. We proposed six different scenarios characterised by various sample sizes, numbers of covariates and relationships between covariates, exposure statuses, and outcomes. We have also illustrated the application of these methods, in which they were used to estimate the efficacy of barbiturates prescribed during the first 24 h of an episode of intracranial hypertension. In the context of GC, for estimating the individual outcome probabilities in two counterfactual worlds, we reported that the super learner tended to outperform the other approaches in terms of both bias and variance, especially for small sample sizes. The support vector machine performed well, but its mean bias was slightly higher than that of the super learner. In the investigated scenarios, G-computation associated with the super learner was a performant method for drawing causal inferences, even from small sample sizes.

scholarly journals Output and Expected Returns in Central and Eastern European Countries

2020 ◽  
Vol 54 (4) ◽  
pp. 41
Author(s):  
Jerzy Gajdka ◽  
Piotr Pietraszewski
Keyword(s):  
Stock Market ◽  
Degrees Of Freedom ◽  
The United States ◽  
Theoretical Background ◽  
Small Sample ◽  
Stock Index ◽  
Stock Market Returns ◽  
Overlapping Data ◽  
State Of The Economy ◽  
Eastern European Countries

<p><strong>Theoretical background</strong>: Although some controversy remains, some aspects of the predictability of aggregate stock market returns in the United States and other industrialized countries appear to be relatively well established. Intertemporal asset pricing models based on the paradigm of investor rationality and market efficiency imply that various macro variables describing the state of the economy may forecast future returns on the aggregate stock market.</p><p><strong>Purpose of the article</strong>: The aim of the article is to present the results of a preliminary study which set out to determine whether the ratio of the stock index to the aggregate output in the economy and future rates of return in the aggregate stock markets in Central and Eastern Europe are significantly related to each other over different time horizons.</p><p><strong>Research methods</strong>: Heteroskedasticity and autocorrelation-consistent estimators with a small sample degrees of freedom adjustment were used in regressions to track overlapping data problem and small sample bias.</p><p><strong>Main findings</strong>: The analysis of the key market indices has shown that they explain much of the variation in the long-horizon future cumulative returns, as well as in cumulative excess returns.</p>

scholarly journals Adaptive Learning Mode of a Multimedia-based "English Literature" Learning System

2017 ◽  
Vol 12 (01) ◽  
pp. 71 ◽  
Author(s):  
Jie Mei ◽  
Yanhong Guo ◽  
Xiaokun Li
Keyword(s):  
Adaptive Learning ◽  
English Literature ◽  
Learning System ◽  
The Self ◽  
Independent Learning ◽  
Learning Ability ◽  
Teaching Model ◽  
Learning Situation ◽  
Teaching Pattern ◽  
Self Adaptive

In this paper, a multimedia-based English pronunciation learning system was designed. On this basis, a self-adaptive learning mode which consists of the teaching mode and the independent learning mode was proposed. The self-adaptive teaching model uses corpus technology and covers the exploratory “3I” (Illustration-Interaction-Induction) teaching model, thereby changing the traditional teaching pattern of “spoon-feeding”; when it comes to the independent learning mode, the self-adaptive system can automatically set corresponding learning tasks according to the learning situation of students, to improve the autonomy and differences of students’ self-learning. At the same time, the approach of comparative teaching was especially adopted to test the validity of this system and the learning mode. Specifically, the exquisite course of “English Literature” for students of Grade 2015 majoring in English was selected as the experimental group, to compare with the learning situation of their counterparts of Grade 2014 in the last year. The results show that the learning mode is remarkable in its teaching practicality, could bring a significant effect on improving teaching efficiency and students’ independent learning ability, and enjoys a high research value and a promising application prospect.

Arch Dam Deformation Prediction Model Based on PSO-SVM

2013 ◽  
Vol 351-352 ◽  
pp. 1306-1311 ◽  
Author(s):  
Jing Yang Liu ◽  
He Zhi Liu
Keyword(s):  
Prediction Model ◽  
Large Scale ◽  
Small Sample ◽  
Arch Dam ◽  
Support Vector ◽  
Model Parameters ◽  
Deformation Prediction ◽  
Dam Foundation ◽  
Model Based ◽  
Dam Deformation

Arch dam has gradually evolved as one of dam type as main large-scale hydraulic project, dam deformation prediction is an important part of dam safety monitoring, and it is difficult to forecast because of the complicated nonlinear characteristics of the monitoring data. Support Vector Machine (SVM) could solve the small sample, nonlinear high dimension problem due to the excellent generalization ability, and hence it has been widely used in the forecast of arch dam deformation. However, the forecast results considerably depend on the choice of SVM model parameters. In this paper, Particle Swarm Optimization (PSO), which has the characteristic of fast global optimization, was applied to optimize the parameters in SVM, and then the dam deformation prediction model based on PSO-SVM could be established. The model is applied to a certain arch dam foundation prediction. The accuracy of this employed approach was examined by comparing it with multiple regression method. In a word, the experimental results indicate that the proposed method based on PSO-SVM can be used in arch dam deformation prediction.

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