The valve is a key control component in the oil and gas transportation system, which, due to environment, transmission medium, other factors, susceptible internal leakage, resulting failure. Conventional testing methods cannot judge service life of valves. Therefore, it important carry out prediction research for safety. In this work, method based on PCA-PSO-LSSVM algorithm proposed. main facto...

Problem statement: As the performance of Least Squares Support Vector Machines (LSSVM) is highly rely on its value of regularization parameter, γ and kernel parameter, σ, manmade approach is clearly not an appropriate solution since it may lead to blindness in certain extent. In addition, this technique is time consuming and unsystematic, which consequently affect the generalization performance...

In this paper, Artificial Bee Colony (ABC) algorithm which inspired from the behavior of honey bees swarm is presented. ABC is a stochastic population-based evolutionary algorithm for problem solving. ABC algorithm, which is considered one of the most recently swarm intelligent techniques, is proposed to optimize least square support vector machine (LSSVM) to predict the daily stock prices. The...

this paper concentrates on a new procedure which experimentally recognises gears and bearings faults of a typical gearbox system using a least square support vector machine (lssvm). two wavelet selection criteria maximum energy to shannon entropy ratio and maximum relative wavelet energy are used and compared to select an appropriate wavelet for feature extraction. the fault diagnosis method co...

This paper presents how commonly used machine learning classifiers can be analyzed using a common framework of convex optimization. Four classifier models, the Support Vector Machine (SVM), the Least-Squares SVM (LSSVM), the Extreme Learning Machine (ELM), and the Margin Loss ELM (MLELM) are discussed to demonstrate how specific parametrizations of a general problem statement affect the classif...

Prediction and parameter optimization are effective methods for mine personnel to control blast-induced ground vibration. However, the challenge of prediction lies in multi-factor multi-effect nature open-pit blasting. This study proposes a hybrid intelligent model predict vibrations using least-squares support vector machine (LSSVM) optimized by particle swarm algorithm (PSO). Meanwhile, multi...

One of the challenging problems in the Oil & Gas industry is accurate and reliable multiphase flow rate measurement in a three-phase flow. Application of methods with minimized uncertainty is required in the industry. Previous developed correlations for two-phase flow are complex and not capable of three-phase flow. Hence phase behavior identification in different conditions to designing and mo...

In this paper, we introduce a new technique for the separation of physical and spurious modes based on an initial clustering in frequency-damping space, followed by a self-learning classification algorithm. For the classification, Least Squares Support Vector Machines are used, a Least Squares version of the theory of Support Vector Machines which maps the classification problem to a high-dimen...

In the construction industry, evaluating the financial status of a contractor is a challenging task due to the myriad of the input data as well as the complexity of the working environment. This article presents a novel hybrid intelligent approach named as Evolutionary Least Squares Support Vector Machine Inference Model for Predicting Contractor Default Status (ELSIM-PCDS). The proposed ELSIM-...