scholarly journals State of Health and Charge Estimation Based on Adaptive Boosting integrated with particle swarm optimization/support vector machine (AdaBoost-PSO-SVM) Model for Lithium-ion Batteries

2022 ◽  
pp. ArticleID:220212
Author(s):  
Ran Li ◽  
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Lithium Ion Batteries ◽  
Particle Swarm ◽  
Lithium Ion ◽  
Support Vector ◽  
State Of Health ◽  
Swarm Optimization ◽  
Adaptive Boosting ◽  
Svm Model

Application of a new hybrid particle swarm optimization-mixed kernels function-based support vector machine model for reservoir porosity prediction: A case study in Jacksonburg-Stringtown oil field, West Virginia, USA

2019 ◽  
Vol 7 (1) ◽  
pp. T97-T112 ◽  
Author(s):  
Zhi Zhong ◽  
Timothy R. Carr
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Absolute Error ◽  
Kernel Functions ◽  
Oil Field ◽  
Support Vector ◽  
Well Log ◽  
Swarm Optimization ◽  
Svm Model

Porosity is a fundamental property that characterizes the storage capability of fluid and gas-bearing formations in a reservoir. An accurate porosity value can be measured from core samples in the laboratory; however, core analysis is expensive and time consuming. Well-log data can be used to calculate porosity, but the availability of log suites is often limited in mature fields. Therefore, robust porosity prediction requires integration of core-measured porosity with available well-log suites to control for changes in lithology and fluid content. A support vector machine (SVM) model with mixed kernel function (MKF) is used to construct the relationship between limited conventional well-log suites and sparse core data. Porosity is the desired output, and two conventional well-log responses (gamma ray [GR] and bulk density) and three well-log-derived parameters (the slope of GR, the slope of density, and [Formula: see text]) are input parameters. A global stochastic searching algorithm, particle swarm optimization (PSO), is applied to improve the efficiency of locating the appropriate values of five control parameters in MKF-SVM model. The results of SVM with different traditional kernel functions were compared, and the MKF-SVM model provided an improvement over the traditional SVM model. To confirm the advantage of the hybrid PSO-MKF-SVM model, the results from three models: (1) radial basis function (RBF)-based least-squares SVM, (2) multilayer perceptron artificial neural network (ANN), and (3) RBF ANN, are compared with the result of the hybrid PSO-MKF-SVM model. The results indicate that the hybrid PSO-MKF-SVM model improves porosity prediction with the highest correlation coefficient ([Formula: see text] of 0.9560), the highest coefficient of determination ([Formula: see text] of 0.9140), the lowest root-mean-square error (1.6505), average absolute error value (1.4050), and maximum absolute error (2.717).

Assessing the Freshness of Meat by Using Quantum-Behaved Particle Swarm Optimization and Support Vector Machine

2013 ◽  
Vol 76 (11) ◽  
pp. 1916-1922
Author(s):  
XIAO GUAN ◽  
JING LIU ◽  
QINGRONG HUANG ◽  
JINGJUN LI
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Ph Value ◽  
Optimal Parameter ◽  
Particle Swarm ◽  
Plate Count ◽  
Support Vector ◽  
Swarm Optimization ◽  
Svm Model ◽  
Aerobic Plate Count

To improve the performance of meat freshness identification systems, we present a new identification method based on quantum-behaved particle swarm optimization (QPSO) and the support vector machine (SVM). Fresh pork, beef, mutton, and shrimp samples were stored in a hypobaric chamber for several days, and the conventional indices of meat freshness, including total volatile basic nitrogen content, aerobic plate count, pH value, and sensory scores, were determined to achieve the identification of sample freshness. However, the experiments showed that it was difficult to obtain an ideal freshness assessment by any single physicochemical or sensory property. Therefore, SVM was introduced to use these data to build a freshness model. Furthermore, QPSO was proposed to seek the optimal parameter combination of SVM. The experimental results indicated that the hybrid SVM model with QPSO could be used to predict meat freshness with 100% classification accuracy.

Fault Diagnosis of PT Fuel System Based on Particle Swarm Optimization-Support Vector Machine

2011 ◽  
Vol 121-126 ◽  
pp. 2809-2813
Author(s):  
Dong Wang ◽  
Xin Qing Wang ◽  
Xiao Long Wang ◽  
Sheng Liang ◽  
Yang Zhao
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Support Vector ◽  
Fuel System ◽  
Swarm Optimization ◽  
Pressure Signal ◽  
Svm Model

In order to overcome the difficulty in selecting parameters of support vector machine (SVM) when modeling the PT fuel system fault diagnosis, SVM optimized by particle swarm optimization (PSO) algorithm was proposed. The PSO-SVM model was established and the fault multi-classifiers of the SVM were got. The pressure signal of the PT fuel inlet and outlet at different rotational speed and conditions was collected. The algorithm of PSO-SVM was used to train and recognize the pressure signal. The result of experiment confirms the validity of this method through comparison of the BP-NN, SVM and the PSO-SVM.

Analysis of Feature Reduction Techniques for Online News Popularity Prediction

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Shivangi Bhargava ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Online News ◽  
Feature Reduction ◽  
Support Vector ◽  
Swarm Optimization

News popularity is the maximum growth of attention given for particular news article. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.

An Improved Intelligent Approach to Enhance the Sentiment Classifier for Knowledge Discovery Using Machine Learning

2020 ◽  
Vol 10 (4) ◽  
pp. 582-593
Author(s):  
Midde Venkateswarlu Naik ◽  
D. Vasumathi ◽  
A.P. Siva Kumar
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Global Warming ◽  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Sentiment Classification ◽  
Support Vector ◽  
Swarm Optimization

Aims: The proposed research work is on an evolutionary enhanced method for sentiment or emotion classification on unstructured review text in the big data field. The sentiment analysis plays a vital role for current generation of people for extracting valid decision points about any aspect such as movie ratings, education institute or politics ratings, etc. The proposed hybrid approach combined the optimal feature selection using Particle Swarm Optimization (PSO) and sentiment classification through Support Vector Machine (SVM). The current approach performance is evaluated with statistical measures, such as precision, recall, sensitivity, specificity, and was compared with the existing approaches. The earlier authors have achieved an accuracy of sentiment classifier in the English text up to 94% as of now. In the proposed scheme, an average accuracy of sentiment classifier on distinguishing datasets outperformed as 99% by tuning various parameters of SVM, such as constant c value and kernel gamma value in association with PSO optimization technique. The proposed method utilized three datasets, such as airline sentiment data, weather, and global warming datasets, that are publically available. The current experiment produced results that are trained and tested based on 10- Fold Cross-Validations (FCV) and confusion matrix for predicting sentiment classifier accuracy. Background: The sentiment analysis plays a vital role for current generation people for extracting valid decisions about any aspect such as movie rating, education institute or even politics ratings, etc. Sentiment Analysis (SA) or opinion mining has become fascinated scientifically as a research domain for the present environment. The key area is sentiment classification on semi-structured or unstructured data in distinguish languages, which has become a major research aspect. User-Generated Content [UGC] from distinguishing sources has been hiked significantly with rapid growth in a web environment. The huge user-generated data over social media provides substantial value for discovering hidden knowledge or correlations, patterns, and trends or sentiment extraction about any specific entity. SA is a computational analysis to determine the actual opinion of an entity which is expressed in terms of text. SA is also called as computation of emotional polarity expressed over social media as natural text in miscellaneous languages. Usually, the automatic superlative sentiment classifier model depends on feature selection and classification algorithms. Methods: The proposed work used Support vector machine as classification technique and particle swarm optimization technique as feature selection purpose. In this methodology, we tune various permutations and combination parameters in order to obtain expected desired results with kernel and without kernel technique for sentiment classification on three datasets, including airline, global warming, weather sentiment datasets, that are freely hosted for research practices. Results: In the proposed scheme, The proposed method has outperformed with 99.2% of average accuracy to classify the sentiment on different datasets, among other machine learning techniques. The attained high accuracy in classifying sentiment or opinion about review text proves superior effectiveness over existing sentiment classifiers. The current experiment produced results that are trained and tested based on 10- Fold Cross-Validations (FCV) and confusion matrix for predicting sentiment classifier accuracy. Conclusion: The objective of the research issue sentiment classifier accuracy has been hiked with the help of Kernel-based Support Vector Machine (SVM) based on parameter optimization. The optimal feature selection to classify sentiment or opinion towards review documents has been determined with the help of a particle swarm optimization approach. The proposed method utilized three datasets to simulate the results, such as airline sentiment data, weather sentiment data, and global warming data that are freely available datasets.

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