Steel Pip Corrosion Forecasting Based on Support Vector Machine

2012 ◽  
Vol 166-169 ◽  
pp. 1002-1006
Author(s):  
Guang Yue Ma
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Bp Neural Network ◽  
Learning Algorithm ◽  
Support Vector ◽  
Risk Minimization ◽  
Prediction Ability ◽  
Structural Risk ◽  
Better Than ◽  
Local Extreme

BP neural network has some shorcomings,such as local extreme. Support vector machine is a novel statistical learning algorithm,which is based on the principle of structural risk minimization. In the paper, support vector machine is used to perform steel pip corrosion forecasting.The collected steel pip corrosion forecasting experimental data are given,among which corrosion deeps from 8ths to 11ths are used to test the proposed prediction model. BP neural network is applied to steel pip corrosion deep forecasting,which is used to compare with support vector machine to show the superiority of support vector machine in steel pip corrosion forecasting.The comparison of the prediction error of steel pip corrosion deep between support vector machine and BP neural network is given. It can be seen that the prediction ability for steel pip corrosion deep of support vector machine is better than that of BP neural network

Application of Support Vector Machine in Springback Forecasting for Steel Structure

2012 ◽  
Vol 166-169 ◽  
pp. 1366-1369
Author(s):  
Jian Guo Chen ◽  
Zhao Guang Li
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Friction Coefficient ◽  
Bp Neural Network ◽  
Prediction Error ◽  
Steel Structure ◽  
Support Vector ◽  
Springback Prediction ◽  
Better Than

Support vector machine is applied to springback forecasting for steel structure in the paper. In the steel structure, pressure-pad-force, friction coefficient and die filleted corner have a certain influence on springback amount.We employ BP neural network to compare with support vector machine to show the superiority of support vector machine in this study. Finally,we give the comparison of the prediction error of springback for steel structure between support vector machine and BP neural network. Evidently,the springback prediction for steel structure of support vector machine is better than that of BP neural network.

A Wavelet Kernel-Based Support Vector Machine for Communication Network Intrusion Detection

2014 ◽  
Vol 989-994 ◽  
pp. 4474-4477
Author(s):  
Ying Zhan
Keyword(s):  
Neural Network ◽  
Communication Network ◽  
Support Vector Machine ◽  
Intrusion Detection ◽  
Bp Neural Network ◽  
Network Intrusion Detection ◽  
Support Vector ◽  
Detection Accuracy ◽  
Network Intrusion ◽  
Better Than

This study is to propose a wavelet kernel-based support vector machine (SVM) for communication network intrusion detection. The common intrusion types of communication network mainly include DOS, R2L, U2R and Probing. SVM, BP neural network are used to compare with the proposed wavelet kernel-based SVM method to show the superiority of wavelet kernel-based SVM. The detection accuracy for communication network intrusion of wavelet kernel-based SVM is 96.67 %, the detection accuracy for communication network intrusion of SVM is 90.83%, and the detection accuracy for communication network intrusion of BP neural network is 86.67%.It can be seen that the detection accuracy for communication network intrusion of wavelet kernel-based SVM is better than that of SVM or BP neural network.

Civil Aeroengine Fault Diagnosis Based on Fuzzy Least Square Support Vector Machine

2011 ◽  
Vol 130-134 ◽  
pp. 2047-2050 ◽  
Author(s):  
Hong Chun Qu ◽  
Xie Bin Ding
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Coefficient Matrix ◽  
Least Square ◽  
Support Vector ◽  
Influence Coefficient ◽  
Structural Risk ◽  
Better Than

SVM(Support Vector Machine) is a new artificial intelligence methodolgy, basing on structural risk mininization principle, which has better generalization than the traditional machine learning and SVM shows powerfulability in learning with limited samples. To solve the problem of lack of engine fault samples, FLS-SVM theory, an improved SVM, which is a method is applied. 10 common engine faults are trained and recognized in the paper.The simulated datas are generated from PW4000-94 engine influence coefficient matrix at cruise, and the results show that the diagnostic accuracy of FLS-SVM is better than LS-SVM.

scholarly journals PREDICTION OF PASSENGER FLOW ON THE HIGHWAY BASED ON THE LEAST SQUARE SUPPOERT VECTOR MACHINE / MAŽIAUSIŲ KVADRATŲ ATRAMINIŲ VEKTORIŲ METODO TAIKYMAS KELEIVIŲ SRAUTUI GREITKELYJE PROGNOZUOTI / ПРИМЕНЕНИЕ МЕТОДА ОПОРНЫХ ВЕКТОРОВ С КВАДРАТИЧНОЙ ФУНКЦИЕЙ ПОТЕРЬ ДЛЯ ПРОГНОЗИРОВАНИЯ ПАССАЖИРСКИХ ПОТОКОВ НА АВТОМАГИСТРАЛЯХ

Transport ◽  
2011 ◽  
Vol 26 (2) ◽  
pp. 197-203 ◽  
Author(s):  
Yanrong Hu ◽  
Chong Wu ◽  
Hongjiu Liu
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Least Square ◽  
Small Samples ◽  
Support Vector ◽  
Risk Minimization ◽  
Structural Risk Minimization ◽  
Passenger Flow ◽  
Regressive Model ◽  
Structural Risk

A support vector machine is a machine learning method based on the statistical learning theory and structural risk minimization. The support vector machine is a much better method than ever, because it may solve some actual problems in small samples, high dimension, nonlinear and local minima etc. The article utilizes the theory and method of support vector machine (SVM) regression and establishes the regressive model based on the least square support vector machine (LS-SVM). Through predicting passenger flow on Hangzhou highway in 2000–2008, the paper shows that the regressive model of LS-SVM has much higher accuracy and reliability of prediction, and therefore may effectively predict passenger flow on the highway. Santrauka Atraminių vektorių metodas (Support Vector Machine – SVM) yra skaičiuojamasis metodas, paremtas statistikos teorija, struktūriniu požiūriu mažinant riziką. SVM metodas, palyginti su kitais metodais, yra patikimesnis metodas, nes juo remiantis galima išspręsti realias problemas, esant įvairioms sąlygoms. Tyrimams naudojama SVM metodo regresijos teorija ir sukuriamas regresinis modelis, kuris grindžiamas mažiausių kvadratų atraminių vektorių metodu (Least Squares Support Vector Machine – LS-SVM). Straipsnio autoriai prognozuoja keleivių srautą Hangdžou (Kinija) greitkelyje 2000–2008 m. Gauti rezultatai rodo, kad regresinis LS-SVM modelis yra labai tikslus ir patikimas, todėl gali būti efektyviai taikomas keleivių srautams prognozuoti greitkeliuose. Резюме Метод опорных векторов (Support Vector Machine – SVM) – это набор аналогичных алгоритмов вида «обучение с учителем», использующихся для задач классификации и регрессионного анализа. Метод SVM принадлежит к семейству линейных классификаторов. Основная идея метода SVM заключается в переводе исходных векторов в пространство более высокой размерности и поиске разделяющей гиперплоскости с максимальным зазором в этом пространстве. Алгоритм работает в предположении, что чем больше разница или расстояние между параллельными гиперплоскостями, тем меньше будет средняя ошибка классификатора. В сравнении с другими методами метод SVM более надежен и позволяет решать проблемы с различными условиями. Для исследования был использован метод SVM и регрессионный анализ, затем создана регрессионная модель, основанная на методе опорных векторов с квадратичной функцией потерь (Least Squares Support Vector Machine – LS-SVM). Авторы прогнозировали пассажирский поток на автомагистрали Ханчжоу (Китай) в 2000–2008 гг. Полученные результаты показывают, что регрессионная модель LS-SVM является надежной и может быть применена для прогнозирования пассажирских потоков на других магистралях.

scholarly journals Real-time flood forecast using the coupling support vector machine and data assimilation method

2013 ◽  
Vol 16 (5) ◽  
pp. 973-988 ◽  
Author(s):  
Xiao-Li Li ◽  
Haishen Lü ◽  
Robert Horton ◽  
Tianqing An ◽  
Zhongbo Yu
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Time Scale ◽  
Support Vector ◽  
Flood Forecast ◽  
Risk Minimization ◽  
Svm Model ◽  
The Impact ◽  
Assimilation Time ◽  
Better Than

An accurate and real-time flood forecast is a crucial nonstructural step to flood mitigation. A support vector machine (SVM) is based on the principle of structural risk minimization and has a good generalization capability. The ensemble Kalman filter (EnKF) is a proven method with the capability of handling nonlinearity in a computationally efficient manner. In this paper, a type of SVM model is established to simulate the rainfall–runoff (RR) process. Then, a coupling model of SVM and EnKF (SVM + EnKF) is used for RR simulation. The impact of the assimilation time scale on the SVM + EnKF model is also studied. A total of four different combinations of the SVM and EnKF models are studied in the paper. The Xinanjiang RR model is employed to evaluate the SVM and the SVM + EnKF models. The study area is located in the Luo River Basin, Guangdong Province, China, during a nine-year period from 1994 to 2002. Compared to SVM, the SVM + EnKF model substantially improves the accuracy of flood prediction, and the Xinanjiang RR model also performs better than the SVM model. The simulated result for the assimilation time scale of 5 days is better than the results for the other cases.

scholarly journals Beam Structure Damage Identification Based on BP Neural Network and Support Vector Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Bo Yan ◽  
Yao Cui ◽  
Lin Zhang ◽  
Chao Zhang ◽  
Yongzhi Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Bp Neural Network ◽  
Damage Identification ◽  
Support Vector ◽  
Single Crack ◽  
Beam Structure ◽  
Strain Mode ◽  
Structure Damage ◽  
Model Based

It is not easy to find marine cracks of structures by directly manual testing. When the cracks of important components are extended under extreme offshore environment, the whole structure would lose efficacy, endanger the staff’s safety, and course a significant economic loss and marine environment pollution. Thus, early discovery of structure cracks is very important. In this paper, a beam structure damage identification model based on intelligent algorithm is firstly proposed to identify partial cracks in supported beams on ocean platform. In order to obtain the replacement mode and strain mode of the beams, the paper takes simple supported beam with single crack and double cracks as an example. The results show that the difference curves of strain mode change drastically only on the injured part and different degrees of injury would result in different mutation degrees of difference curve more or less. While the model based on support vector machine (SVM) and BP neural network can identify cracks of supported beam intelligently, the methods can discern injured degrees of sound condition, single crack, and double cracks. Furthermore, the two methods are compared. The results show that the two methods presented in the paper have a preferable identification precision and adaptation. And damage identification based on support vector machine (SVM) has smaller error results.

Fault Diagnosis for Electrical Control System of Automobile Based on Support Vector Machine

2014 ◽  
Vol 666 ◽  
pp. 203-207
Author(s):  
Jian Hua Cao
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Control System ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Experimental Results ◽  
Support Vector ◽  
Electrical Control ◽  
Diagnosis Method ◽  
The Common

This paper is to present a fault diagnosis method for electrical control system of automobile based on support vector machine. We collect the common fault states of electrical control system of automobile to analyze the fault diagnosis ability of electrical control system of automobile based on support vector machine. It can be seen that the accuracy of fault diagnosis for electrical control system of automobile by support vector machine is 92.31%; and the accuracy of fault diagnosis for electrical control system of automobile by BP neural network is 80.77%. The experimental results show that the accuracy of fault diagnosis for electrical control system of automobile of support vector machine is higher than that of BP neural network.

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