Application of Support Vector Machines to the Tracking Control of Piezoelectric Actuators

2012 ◽  
Vol 459 ◽  
pp. 82-85
Author(s):  
Shu Yan Yang ◽  
Hai Feng Wang
Keyword(s):  
Support Vector Machines ◽  
Tracking Control ◽  
Hybrid Approach ◽  
Nonlinear Behavior ◽  
Piezoelectric Actuators ◽  
Experimental Results ◽  
Support Vector ◽  
Output Displacement ◽  
Vector Machines ◽  
Final Output

In this paper, a hybrid approach associated the Preisach concept with support vector machines (SVM) is brought forward to identify and predict the nonlinear behavior of piezoelectric actuators (PA). Preisach concept is used to construct mesh nodes in the Preisach plane and determine the final output displacement of PA. SVM is trained based on the mesh nodes and provides favorable generalization ability in the Preisach plane. Experimental results validate the proposed method.

scholarly journals INPUTING STUDENTS’ SCORE BASED ON GIST FEATURES, SUPPORT VECTOR MACHINES AND TESSERACT

2020 ◽  
Vol 1 (41) ◽  
pp. 77-85
Author(s):  
Hau Hung Nguyen
Keyword(s):  
Support Vector Machines ◽  
High Performance ◽  
Regions Of Interest ◽  
Experimental Results ◽  
Support Vector ◽  
Vocational College ◽  
Vector Machines ◽  
Handwritten Digit

Handwriting recogination plays an important role in data inputing and processing in the practice. This attracts much attention of many researchers in different fields. In this paper, a new algorithm is proposed by basing on GIST features, Support Vector Machines (SVM) and Tesseract for entering the score on students’ transcript form at Soc Trang Vocational College. The algorithm consists of two main works, i.e., recognizing students’code and recogziing handwritten digit. In the proposed algorithm, all regions of interest are determined and extract their dictint features with using tesseract and GIST. Then, these features are classified by SVM mechanism. Experimental results demonstrated that the proposed algorithm obtained high performance with accuracy up to 96,57% for students’ code and 93,55% for Handwritting scores. Average time was 7,9s per one transcript.

Application of Fuzzy Least Squares Support Vector Machines in Landslide Deformation Prediction

2012 ◽  
Vol 594-597 ◽  
pp. 2402-2405 ◽  
Author(s):  
Wei Chen ◽  
Xiao Xiao ◽  
Jian Zhang
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Experimental Results ◽  
Support Vector ◽  
Deformation Prediction ◽  
Vector Machines ◽  
Landslide Deformation ◽  
Accuracy Of Prediction

Aiming at the problem that the Least Squares Support Vector Machines(LSSVM) was sensitive to noises or outliers, fuzzy idea was used to the Least Squares Support Vector Machines.The Fuzzy Least Squares Support Vector Machines(FLSSVM) was proposed and was applied to the Landslide Deformation Prediction. Experimental results show that this method can improve the accuracy of prediction and be effectively applied to landslide deformation prediction.

scholarly journals Rescale-Invariant SVM for Binary Classification

2017 ◽  
Author(s):  
Mojtaba Montazery ◽  
Nic Wilson
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Support Vector Machines ◽  
Binary Classification ◽  
Experimental Results ◽  
Support Vector ◽  
Computation Method ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Vector Machines

Support Vector Machines (SVM) are among the most well-known machine learning methods, with broad use in different scientific areas. However, one necessary pre-processing phase for SVM is normalization (scaling) of features, since SVM is not invariant to the scales of the features’ spaces, i.e., different ways of scaling may lead to different results. We define a more robust decision-making approach for binary classification, in which one sample strongly belongs to a class if it belongs to that class for all possible rescalings of features. We derive a way of characterising the approach for binary SVM that allows determining when an instance strongly belongs to a class and when the classification is invariant to rescaling. The characterisation leads to a computation method to determine whether one sample is strongly positive, strongly negative or neither. Our experimental results back up the intuition that being strongly positive suggests stronger confidence that an instance really is positive.

Applying More Initial Groups to the Semi-Supervised Clustering Based on one-Class Support Vector Machines

2013 ◽  
Vol 278-280 ◽  
pp. 1215-1220
Author(s):  
Lei Gu
Keyword(s):  
Support Vector Machines ◽  
Experimental Results ◽  
Support Vector ◽  
Optimal Solutions ◽  
Number Of Clusters ◽  
Supervised Clustering ◽  
Vector Machines ◽  
Traditional Algorithm

The traditional semi-supervised clustering based on one-class support vector machines used some labeled data called seeds for the clustering initialization. These seeds were partitioned into several initial groups according to their labels and the number of initial groups was equal to the number of clusters. However, the traditional semi-supervised clustering based on one-class support vector machines is sensitive to the initial groups and often obtained the local optimal solutions. In this paper, more initial groups produced by seeds are applied to the traditional semi-supervised clustering based on one-class support vector machines to get more local optimal solutions and the proposed algorithm can combine multiple local optimal solutions to obtain the better clustering performance at last. To investigate the effectiveness of our approach, experiments are done on two real datasets. Experimental results show that the presented method can improve the clustering accuracies compared to the traditional algorithm.

MULTIPLE DECISION TEMPLATES WITH ADAPTIVE FEATURES FOR FINGERPRINT CLASSIFICATION

2007 ◽  
Vol 21 (08) ◽  
pp. 1323-1338 ◽  
Author(s):  
JUN-KI MIN ◽  
SUNG-BAE CHO
Keyword(s):  
Support Vector Machines ◽  
Feature Vector ◽  
Experimental Results ◽  
Classification Method ◽  
Support Vector ◽  
Fingerprint Classification ◽  
Vector Machines ◽  
Fingerprint Database

This paper proposes a novel fingerprint classification method using multiple decision templates of Support Vector Machines (SVMs) with adaptive features. In order to overcome intra-class and inter-class ambiguities of fingerprints, the proposed method extracts a feature vector from an adaptively detected feature region and classifies the feature vector using SVMs. The outputs of the SVMs are then combined by multiple decision templates that make several per class. Experimental results on NIST4 fingerprint database revealed the effectiveness and validity of the proposed method for fingerprint classification.

scholarly journals Hybridizing support vector machines into genetic algorithm for key factor exploration in core competence evaluation of aviation manufacturing enterprises

Filomat ◽  
2016 ◽  
Vol 30 (15) ◽  
pp. 4191-4198
Author(s):  
Linwei Zhai ◽  
Jian Qin ◽  
Lean Yu
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machines ◽  
Core Competence ◽  
Hybrid Approach ◽  
Aviation Industry ◽  
Support Vector ◽  
Manufacturing Enterprises ◽  
The Core ◽  
Vector Machines ◽  
Key Factor

In the core competence comprehensive evaluation of aviation manufacturing enterprises, exploring the key factors affecting core competence is crucial to improve the competitiveness of the aviation manufacturing enterprises. In this paper, a novel hybrid approach integrating genetic algorithm (GA) and support vector machines (SVM) is proposed to conduct the key factor exploration tasks in the core competitiveness evaluation of aviation manufacturing enterprises. In the proposed hybrid GA-SVM approach, the GA is used for key factor exploration, while SVM is used to calculate the fitness function of the GA method. Using the survey data from Aviation Industry Corporation of China (AVIC), some experiments analysis is conducted to test the effectiveness of the proposed hybrid approach. Empirical results demonstrate that the proposed hybrid GA-SVM approach can be used as an alternative solution to key factor exploration.

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