Using Support Vector Machine for Prediction Dynamic Voltage Collapse in an Actual Power System

Abstract—This paper presents dynamic voltage collapse prediction on an actual power system using support vector machines. Dynamic voltage collapse prediction is first determined based on the PTSI calculated from information in dynamic simulation output. Simulations were carried out on a practical 87 bus test system by considering load increase as the contingency. The data collected from the time domain simulation is then used as input to the SVM in which support vector regression is used as a predictor to determine the dynamic voltage collapse indices of the power system. To reduce training time and improve accuracy of the SVM, the Kernel function type and Kernel parameter are considered. To verify the effectiveness of the proposed SVM method, its performance is compared with the multi layer perceptron neural network (MLPNN). Studies show that the SVM gives faster and more accurate results for dynamic voltage collapse prediction compared with the MLPNN. Keywor ds —Dynamic voltage collapse, prediction, artificial neural network, support vector machines

Pattern recognition system based on support vector machines: HIV-1 integrase inhibitors application

Support Vector Machines (SVM) represent one of the most promising Machine Learning (ML) tools that can be applied to develop a predictive Quantitative Structure-Activity Relationship (QSAR) models using molecular descriptors. The performance and predictive power of support vector machines (SVM) for regression problems in quantitative structure-activity relationship were investigated. The SVM results are superior to those obtained by artificial neural network and multiple linear regression. These results indicate that the SVM model with the kernel radial basis function can be used as an alternative tool for regression problems in quantitative structure-activity relationship. Keywords: Support Vector Machines; Artificial Neural Network; Quantitative Structure-Activity Relationship

Classifying LEP Data with Support Vector Algorithms

We have studied the application of different classification algorithms in the analysis of simulated high energy physics data. Whereas Neural Network algorithms have become a standard tool for data analysis, the performance of other classifiers such as Support Vector Machines has not yet been tested in this environment. We chose two different problems to compare the performance of a Support Vector Machine and a Neural Net trained with back-propagation: tagging events of the type e+e- -> ccbar and the identification of muons produced in multihadronic e+e- annihilation events.Comment: 7 pages, 4 figures, submitted to proceedings of AIHENP99, Crete, April 199

Evolutionary Optimization Of Support Vector Machines

Support vector machines are a relatively new approach for creating classifiers that have become increasingly popular in the machine learning community. They present several advantages over other methods like neural networks in areas like training speed, convergence, complexity control of the classifier, as well as a stronger mathematical background based on optimization and statistical learning theory. This thesis deals with the problem of model selection with support vector machines, that is, the problem of finding the optimal parameters that will improve the performance of the algorithm. It is shown that genetic algorithms provide an effective way to find the optimal parameters for support vector machines. The proposed algorithm is compared with a backpropagation Neural Network in a dataset that represents individual models for electronic commerce

Accelerating Deep Learning with Shrinkage and Recall

Deep Learning is a very powerful machine learning model. Deep Learning trains a large number of parameters for multiple layers and is very slow when data is in large scale and the architecture size is large. Inspired from the shrinking technique used in accelerating computation of Support Vector Machines (SVM) algorithm and screening technique used in LASSO, we propose a shrinking Deep Learning with recall (sDLr) approach to speed up deep learning computation. We experiment shrinking Deep Learning with recall (sDLr) using Deep Neural Network (DNN), Deep Belief Network (DBN) and Convolution Neural Network (CNN) on 4 data sets. Results show that the speedup using shrinking Deep Learning with recall (sDLr) can reach more than 2.0 while still giving competitive classification performance.Comment: The 22nd IEEE International Conference on Parallel and Distributed Systems (ICPADS 2016

Application of Soft Computing for the Prediction of Warpage of Plastic Injection

This paper deals with the development of accurate warpage prediction model for plastic injection molded parts using softcomputing tools namely, artificial neural networks and support vector machines. For training, validating and testing of thewarpage model, a number of MoldFlow (FE) analyses have been carried out using Taguchi’s orthogonal array in the designof experimental technique by considering the process parameters such as mold temperature, melt temperature, packing pressure,packing time and cooling time. The warpage values were found by analyses which were done by MoldFlow PlasticInsight (MPI) 5.0 software. The artificial neural network model and support vector machine regression model have beendeveloped using conjugate gradient learning algorithm and ANOVA kernel function respectively. The adequacy of the developedmodels is verified by using coefficient of determination. To judge the ability and efficiency of the models to predictthe warpage values absolute relative error has been used. The finite element results show, artificial neural network modelpredicts with high accuracy compared with support vector machine model

Application of support vector machines for prediction of anti-HIV activity of TIBO Derivatives.

The performance and predictive power of support vector machines (SVM) for regression problems in quantitative structure-activity relationship were investigated. The SVM results are superior to those obtained by artificial neural network and multiple linear regression. These results indicate that the SVM model with the kernel radial basis function can be used as an alternative tool for regression problems in quantitative structure-activity relationship. Keywords: support vector machine (SVM); ANN; QSA