Modeling of Flexible Structures by Means of Least Square Support Vector Machine

2015 ◽  
Author(s):  
Hammam Tamimi ◽  
Dirk Söffker
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Flexible Structures ◽  
Operating Conditions ◽  
Least Square ◽  
Support Vector ◽  
Suitable Model ◽  
Support Vector Machine Algorithm ◽  
Training Time ◽  
Svm Algorithm

This paper investigates modeling of flexible structures by means of the least squares support vector machine (LS-SVM) algorithm. Modeling is the first step to obtain a suitable model-based controller for any given system. Accurate modeling of a flexible structure based on experimental data using LS-SVM algorithm requires less knowledge about the physical system. Least squares support vector machine algorithm can achieve global and unique solution when compared with other soft computing algorithms. Also, LS-SVM algorithm requires less training time. In this paper, the successful use of support vector machine algorithm to model the flexible cantilever is demonstrated. The acquired model is able to provide accurate prediction of the system output under different operating conditions. Experimental results demonstrate the efficiency and high precision of the proposed approach.

Fault Diagnosis for Temperature Signal of Turbine Blade Based on LS-SVM

2013 ◽  
Vol 385-386 ◽  
pp. 580-584 ◽  
Author(s):  
Li Wei Chen ◽  
Chen Dong Wang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Least Squares ◽  
Turbine Blade ◽  
Small Sample ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Operation Speed ◽  
Svm Algorithm ◽  
Temperature Signal

This document discusses the support vector machine (SVM) algorithm, then discusses least squares support vector machine (LS-SVM) algorithm, at the same time, the applications of SVM in the fault diagnosis of temperature signal of turbine blade being discussed, the least squares support vector machine algorithm being used in the research of fault diagnosis, being compared with LVQ neural network, experiments result show the operation speed of the least squares support vector machine algorithm is fast, its generalization ability is stronger, SVM can solve small sample learning problems as well as no-linear, high dimension and local minimization problems in the fault diagnosis of temperature signal of turbine blade.

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 Pulmonary Nodule Recognition Based on Multiple Kernel Learning Support Vector Machine-PSO

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yang Li ◽  
Zhichuan Zhu ◽  
Alin Hou ◽  
Qingdong Zhao ◽  
Liwei Liu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Pulmonary Nodule ◽  
Multiple Kernel Learning ◽  
Pso Algorithm ◽  
Support Vector ◽  
Training Time ◽  
Inertia Weight ◽  
Fitness Value ◽  
Svm Algorithm ◽  
Fitness Curve

Pulmonary nodule recognition is the core module of lung CAD. The Support Vector Machine (SVM) algorithm has been widely used in pulmonary nodule recognition, and the algorithm of Multiple Kernel Learning Support Vector Machine (MKL-SVM) has achieved good results therein. Based on grid search, however, the MKL-SVM algorithm needs long optimization time in course of parameter optimization; also its identification accuracy depends on the fineness of grid. In the paper, swarm intelligence is introduced and the Particle Swarm Optimization (PSO) is combined with MKL-SVM algorithm to be MKL-SVM-PSO algorithm so as to realize global optimization of parameters rapidly. In order to obtain the global optimal solution, different inertia weights such as constant inertia weight, linear inertia weight, and nonlinear inertia weight are applied to pulmonary nodules recognition. The experimental results show that the model training time of the proposed MKL-SVM-PSO algorithm is only 1/7 of the training time of the MKL-SVM grid search algorithm, achieving better recognition effect. Moreover, Euclidean norm of normalized error vector is proposed to measure the proximity between the average fitness curve and the optimal fitness curve after convergence. Through statistical analysis of the average of 20 times operation results with different inertial weights, it can be seen that the dynamic inertial weight is superior to the constant inertia weight in the MKL-SVM-PSO algorithm. In the dynamic inertial weight algorithm, the parameter optimization time of nonlinear inertia weight is shorter; the average fitness value after convergence is much closer to the optimal fitness value, which is better than the linear inertial weight. Besides, a better nonlinear inertial weight is verified.

Optimization of Financial Forecasting Based on Least Squares Support Vector Machine

2013 ◽  
Vol 760-762 ◽  
pp. 1987-1991
Author(s):  
Yun Fa Li
Keyword(s):  
Support Vector Machine ◽  
Simulated Annealing ◽  
Stock Market ◽  
Prediction Model ◽  
Least Squares ◽  
Simulated Annealing Algorithm ◽  
Least Square ◽  
Support Vector ◽  
Stock Investment ◽  
Annealing Algorithm

To master the variation regularity of finance, obtain greater benefits in stock investment. study of the support vector machine and application in prediction of stock market. The simulated annealing algorithm to optimize the least squares support vector machine prediction model, and the least square support vector machine and simulated annealing algorithm is described, given the optimal prediction model. Through the research on the simulation of the Hang Seng Index, shows that this method is simple, fast convergence, the algorithm with high accuracy. Has the actual guiding sense for investors, the stock market of the financial firm to operate.

scholarly journals A Hybrid Least Square Support Vector Machine Model with Parameters Optimization for Stock Forecasting

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Jian Chai ◽  
Jiangze Du ◽  
Kin Keung Lai ◽  
Yan Pui Lee
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Support Vector Machine Model ◽  
Optimization Methods ◽  
Parameters Optimization ◽  
Least Square ◽  
Movement Direction ◽  
Support Vector ◽  
Machine Model ◽  
Mode Decomposition

This paper proposes an EMD-LSSVM (empirical mode decomposition least squares support vector machine) model to analyze the CSI 300 index. A WD-LSSVM (wavelet denoising least squares support machine) is also proposed as a benchmark to compare with the performance of EMD-LSSVM. Since parameters selection is vital to the performance of the model, different optimization methods are used, including simplex, GS (grid search), PSO (particle swarm optimization), and GA (genetic algorithm). Experimental results show that the EMD-LSSVM model with GS algorithm outperforms other methods in predicting stock market movement direction.

Sign in / Sign up

Export Citation Format

Share Document