A genetic algorithm to optimize the adaptive Support Vector Regression model for forecasting the reliability of diesel engine systems

Chato, Lina; Tayeb, Shahab; Latifi, Shahram

doi:10.1109/ccwc.2017.7868462

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…SVM is a classification tool which adopts the principles of SLT and structural risk minimisation principles [88]. The working mechanism of an SVM model is such that a non‐linear function is transformed to a linear function in a high dimensional feature space using certain functions called the kernel functions.…”

Section: Methodsmentioning

confidence: 99%

SVM‐based hybrid approach for corridor‐level travel‐time estimation

Sharmila

Velaga

Kumar

2019

IET Intelligent Transport Systems

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Section: Methodsmentioning

confidence: 99%

SVM‐based hybrid approach for corridor‐level travel‐time estimation

Sharmila

Velaga

Kumar

2019

IET Intelligent Transport Systems

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“…www.ijacsa.thesai.org 2) Genetic algorithm (GA): GA is an evolutionary Algorithm inspired by the mechanism of natural selection based on Charles Darwin's theory [40]. GA was introduced in 1975 at the University of Michigan by John Holland [41]. GA is widely used to solve optimization problems [42].…”

Section: ) Particle Swarm Optimization (Pso): Pso Has Been Developed ...mentioning

confidence: 99%

Hyperparameter Optimization of Support Vector Regression Algorithm using Metaheuristic Algorithm for Student Performance Prediction

Apriyadi¹,

Ermatita²,

Rini³

2023

IJACSA

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Improving student learning performance requires proper preparation and strategy so that it has an impact on improving the quality of education. One of the preparatory steps is to make a prediction modeling of student performance. Accurate student performance prediction models are needed to help teachers develop the potential of diverse students. This research aims to create a predictive model of student performance with hyperparameter optimization in the Support Vector Regression Algorithm. The hyperparameter optimization method used is the Metaheuristic Algorithm. The Metaheuristic Algorithms used in this study are Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). After obtaining the best SVR hyperparameter, the next step is to model student performance predictions, which in this study produced two models, namely PSVR Modeling and GSVR Modeling. The resulting predictive modeling will also be compared with previous researchers' prediction modeling of student performance using five models: Support Vector Regression, Naïve Bayes, Neural Network, Decision Tree, and Random Forest. The regression performance metric parameter, Root Mean Square Error (RMSE), evaluates modeling results. The test results show that predictive student performance using PSVR Modeling produces the smallest RMSE value of 1.608 compared to predictions of student performance by previous researchers so that the proposed prediction model can be used to predict student performance in the future.

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“…First, the characteristic index which reflects the operating condition is selected, and then the operational reliability calculation model based on the characteristic index is established. ird, the reliability of the equipment is predicted based on various prediction algorithms, such as artificial neural network [9], Support Vector Machines (SVMs) [10], Relevance Vector Machines (RVMs) [11], and so on. For the problem of characteristic index selection, the most related characteristic index to the health of the equipment is usually selected.…”

Section: Introductionmentioning

confidence: 99%

Equipment Operational Reliability Evaluation Method Based on RVM and PCA-Fused Features

Zhu

Dong

Feng

2021

Mathematical Problems in Engineering

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Reliability assessment is of great significance in ensuring the safety and reducing maintenance cost of equipment. The traditional statistical method is widely used to estimate the reliability of mass equipment; however, it cannot efficiently predict the overall reliability of single or small batch equipment due to lack of failure data. This paper introduced the operational reliability concept to describe the running condition of single or small batch equipment and proposed a method based on the combination of Relevance Vector Machines (RVMs) and Principal Component Analysis (PCA) to evaluate the operational reliability. Some representative characteristic indexes of operating equipment were firstly selected, and PCA was applied to obtain a hybrid index of the equipment’s running condition. Then, a RVM prediction model was trained to predict the development of the hybrid index and corresponding probability density function (PDF). Based on this, the operational reliability of the equipment was calculated by the interval integral defined by the failure threshold and the predicted value of the hybrid index. The approach was validated using the experimental test conducted on the aero-engine rotor bearings. The results show a good agreement in the evaluations of the failure time between the proposed method and the experimental test.

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A genetic algorithm to optimize the adaptive Support Vector Regression model for forecasting the reliability of diesel engine systems

Cited by 3 publications

References 6 publications

SVM‐based hybrid approach for corridor‐level travel‐time estimation

SVM‐based hybrid approach for corridor‐level travel‐time estimation

Hyperparameter Optimization of Support Vector Regression Algorithm using Metaheuristic Algorithm for Student Performance Prediction

Equipment Operational Reliability Evaluation Method Based on RVM and PCA-Fused Features

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