scholarly journals Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets

2006 ◽  
Vol 1 (1) ◽  
pp. 49 ◽  
Author(s):  
Wun Hua Chen ◽  
Jen Ying Shih ◽  
Soushan Wu
Keyword(s):  
Neural Networks ◽  
Support Vector Machines ◽  
Stock Markets ◽  
Back Propagation ◽  
Support Vector ◽  
Back Propagation Neural Networks ◽  
Vector Machines

scholarly journals RECURRENT NEURAL NETWORKS AND NONLINEAR PREDICTION IN SUPPORT VECTOR MACHINES

2019 ◽  
Vol 2019 (1) ◽  
pp. 33-40 ◽  
Author(s):  
Jennifer S. Raj ◽  
Vijitha Ananthi J
Keyword(s):  
Neural Networks ◽  
Support Vector Machines ◽  
Recurrent Neural Networks ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Support Vector ◽  
Nonlinear Prediction ◽  
Proposed Model ◽  
Vector Machines ◽  
Robust Prediction

The nonlinear regression estimation issues are solved by successful application of a novel neural network technique termed as support vector machines (SVMs). Evaluation of recurrent neural networks (RNNs) can assist in pattern recognition of several real-time applications and reduce the pattern mismatch. This paper provides a robust prediction model for multiple applications. Traditionally, back-propagation algorithms were used for training RNN. This paper predict system reliability by applying SVM learning algorithm to RNN. Comparison of the proposed model is done with the existing systems for analysis of prediction performance. These results indicate that the performance of proposed system exceeds that of the existing ones.

Predicting the strength of Populus spp. clones using artificial neural networks and ε-regression support vector machines (ε-rSVM)

Holzforschung ◽  
2011 ◽  
Vol 65 (6) ◽  
pp. 855-863 ◽  
Author(s):  
Shawn D. Mansfield ◽  
Kyu-Young Kang ◽  
Lazaros Iliadis ◽  
Stavros Tachos ◽  
Stavros Avramidis
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector Machines ◽  
Input Data ◽  
Back Propagation ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Data Set ◽  
Vector Machines ◽  
Artificial Neural

Abstract Wood properties, including bending stiffness and strength, basic density and microfibril angle were experimentally obtained for six aspen and six hybrid poplar clones grown in Western Canada. Data analysis attempted to establish a relationship between wood mechanical properties and intrinsic wood attributes by means of artificial neural networks (ANN) and ε-regression support vector machines (ε-rSVM) employing a 5-fold cross validation approach (5-fold CV). Initial results for strength were acceptable, but require further improvement. Estimations of stiffness results (MOE) were inferior to those of strength (MOR) due to the fact that in several regression cases, the developed model worked well for narrow windows of data, but failed on a large scale due to the high variations in the values of the input data vectors. In such cases, the result is probably the development of regression with uneven performance throughout the input data set, and therefore the modeling capacity is poor. To avoid this predicament, different neural networks with one output neuron were developed in order to estimate either the stiffness or the strength, and at the same time the approximation capabilities of ε-rSVM were employed. In both methods, 5-fold CV was carried out in order to attain a more generalized solution by eliminating the boundary effect phenomena and by avoiding local behavior of the global support vector regression. The resultant models were evaluated by common metrics. The best ANN for the estimation of strength in combination with 5-fold CV, was a modular back propagation with average R2=0.70, and mean root mean square error (MRMSE) equal to 0.19 and mean average percent error (MAPE) equal to 12.5%. The Gaussian kernel 5-fold CV ε-rSVM estimated MOR with similar accuracy. The best 5-fold CV ANN for MOE estimation was a feed forward back propagation one, with average R2=0.60, MRMSE equal to 0.23 and MAPE equal to 41.5%, which was better than all other kernel methods employed.

Audio-Based Condition Monitoring in Milling of the Workpiece Material With the Hardness Variation Using Support Vector Machines and Convolutional Neural Networks

2018 ◽  
Author(s):  
Achyuth Kothuru ◽  
Sai Prasad Nooka ◽  
Rui Liu
Keyword(s):  
Neural Networks ◽  
Support Vector Machines ◽  
Tool Wear ◽  
Support Vector ◽  
Workpiece Material ◽  
Artificial Intelligent ◽  
Tool Condition ◽  
Hardness Variation ◽  
Vector Machines ◽  
Audible Sound

Machining industry has been evolving towards implementation of automation into the process for higher productivity and efficiency. Although many studies have been conducted in the past to develop intelligent monitoring systems in various application scenarios of machining processes, most of them just focused on cutting tools without considering the influence due to the non-uniform hardness of workpiece material. This study develops a compact, reliable, and cost-effective intelligent Tool Condition Monitoring (TCM) model to detect the cutting tool wear in machining of the workpiece material with hardness variation. The generated audible sound signals during the machining process will be analyzed by state of the art artificial intelligent techniques, Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs), to predict the tool condition and the hardness variation of the workpiece. A four-level classification model is developed for the system to detect the tool wear condition based on the width of the flank wear land and hardness variation of the workpiece. The study also involves comparative analysis between two employed artificial intelligent techniques to evaluate the performance of models in predicting the tool wear level condition and workpiece hardness variation. The proposed intelligent models have shown a significant prediction accuracy in detecting the tool wear and from the audible sound into the proposed multi-classification wear class in the end-milling process of non-uniform hardened workpiece.

scholarly journals Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257901
Author(s):  
Yanjing Bi ◽  
Chao Li ◽  
Yannick Benezeth ◽  
Fan Yang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machines ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Computer Assisted ◽  
Long Distance ◽  
Relationship Analysis ◽  
Vector Machines

Phoneme pronunciations are usually considered as basic skills for learning a foreign language. Practicing the pronunciations in a computer-assisted way is helpful in a self-directed or long-distance learning environment. Recent researches indicate that machine learning is a promising method to build high-performance computer-assisted pronunciation training modalities. Many data-driven classifying models, such as support vector machines, back-propagation networks, deep neural networks and convolutional neural networks, are increasingly widely used for it. Yet, the acoustic waveforms of phoneme are essentially modulated from the base vibrations of vocal cords, and this fact somehow makes the predictors collinear, distorting the classifying models. A commonly-used solution to address this issue is to suppressing the collinearity of predictors via partial least square regressing algorithm. It allows to obtain high-quality predictor weighting results via predictor relationship analysis. However, as a linear regressor, the classifiers of this type possess very simple topology structures, constraining the universality of the regressors. For this issue, this paper presents an heterogeneous phoneme recognition framework which can further benefit the phoneme pronunciation diagnostic tasks by combining the partial least square with support vector machines. A French phoneme data set containing 4830 samples is established for the evaluation experiments. The experiments of this paper demonstrates that the new method improves the accuracy performance of the phoneme classifiers by 0.21 − 8.47% comparing to state-of-the-arts with different data training data density.

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