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This paper focuses on the effective analysis of the mine gas emission monitoring data, so as to realize the accurate and reliable mine gas emission prediction. Firstly, a weighted multiple computing models based on parametric t—norm is constructed. And a new mine gas emission combination forecasting method is proposed. The BP neural network model and the support vector machine were used...
This paper uses the GA-SVM inversion model to invert the suspended matter concentration in Longquan Lake. Genetic algorithm (GA) optimizes the parameters of the SVM inversion model to establish the new GA-SVM inversion model, and GA can effectively improve the efficiency and the accuracy of the SVM inversion model. The inversion model was established by using the measured hyperspectral data and suspended...
Binary classification problem is one of the mainstream research in pattern recognition field. This study proposed a modified fruit-fly optimization algorithm (FOA), which can find an eligible begin location of the FOA as starting location before running the FOA's procedure, and in the FOA's processing, the SVM parameters is modified by dynamically updating the location of each fruit-fly and the optimal...
Gray-Box Models which combine a phenomenological model with a black box tool are useful for determining the values of not well known parameters of the model. In this work an indirect strategy for training these gray box models using least-square support vector machine and genetic algorithms is presented. The gray box model was tested in a Continuous Stirred Tank Reactor process with good results (Index...
Aerobics is a new sport in our country. Because of different ethnic groups, there are differentiated competition. How to carry on the quantitative research to our country youth special skill is a major task to improve our athletes comprehensive quality and competitive ability of athletes. In this paper, based on the modern intelligent algorithm, the high precision classification performance of support...
Student performance classification is a challenging task for teacher and stakeholder for better academic planning and management. Data mining can be used to find knowledge from student data to improve the performance of classifying model. Before applying a classification model, feature selection method is proposed in data preprocessing process to find out the most significant and intrinsic features...
Yarn quality prediction plays an important role in modern textile production management. Due to the nonlinearity and non-stationarity of yarn quality indicator series, the accuracy of the commonly used conventional methods, including regression analyses and artificial neural networks (ANN), has been limited. A prediction model based on support vector regression (SVR) is proposed in this paper to solve...
A new guidance law with terminal trajectory angle constraint is designed for bank to turn flight vehicle, which aims at the fixed position ground target. The general form of guidance law with sight angle and sight angle velocity as feedback variables is presented, and the stability of it is proved via finite time convergent stability theory, which makes traditional optimal guidance as a specific example...
In this paper, a hybrid switching particle swarm optimization (SPSO) and support vector machine (SVM) algorithm is proposed for jointly applying to the problem of bankruptcy prediction. The main purpose of this paper is to handle better explanatory power and stability of the SVM. More specifically, a recently developed Switching PSO algorithm is used to find out the optimal parameter values of radial...
In this paper, we propose a novel classification method for hyperspectral imagery, named as HA-PSO-SVM, by integrating harmonic analysis (HA), support vector machine (SVM) and particle swarm optimization (PSO). Pixel in hyperspectral imagery can be represented by amplitude, phase and residual in frequency domain using HA. PSO is used to optimize the parameters for SVM. Its applicability and effects...
Spectrum sensing is a key function for the second users (SUs) to determine availability of a channel in the primary user's (PUs) spectrum in cognitive radio(CR). In order to achieve that, much research of energy detection has been studied, but they play poor performance in low signal-to-noise (SNR) environment. In this paper, we proposed Support Vector Machines (SVM) based on Genetic Algorithms (GA),...
Support vector machine (SVM) classifier has been successfully applied to power transformer fault diagnosis. However, there is no theoretical basis or effective method to select appropriate SVM classifier parameters which have a crucial influence on the classification accuracy. Currently, the main method is cut and try based on experience. In this study, genetic algorithm (GA) is employed to optimize...
This paper proposes a new hybrid technique, Continuous Genetic Algorithm and Least Squares Support Vector Machine to allocate the real power transfer from generators to loads, namely CGA-LSSVM. CGA is used to obtain the optimal value of hyper-parameters of LS-SVM and supervised learning approach is adopted in the training of LS-SVM model. The technique that uses proportional sharing principle (PSP)...
Seawater flue gas Desulfurization (SFGD) was adopted in many coal-fired power plants of littoral for its low cost and high desulfurization efficiency. Operating Parameters would seriously affect SFGD efficiency, the desulfurization efficiency can be improved by adjusting reasonable parameters. this paper applied Least Square Support Machine (LSSVM) to build the studying model of seawater desulfurization...
The support vector machine is a powerful supervised learning algorithm that has been successfully applied to a plenty of fields including text and image recognition, medical diagnosis and so on. The kernel and its parameters optimization, formally known as model selection, is a crucial factor which influences a good tradeoff between bias and variance. To automate model selection of support vector...
With the development of thermal power industry, statistics on the NOx emissions become important. In this paper, based on the traditional support vector machine model, we establish support vector machine model optimized by genetic algorithm, improve the prediction accuracy of SVM model. Use the NOx emissions data from 1995 to 2009, predict the NOx emissions from thermal power plant in the year of...
A new controller for the optimization of the movement of an exploration vehicle is proposed in this paper. Measurements of obstacle and goal's distance and direction are anticipated to be imprecise however, because the performance of ultrasonic sensors is degraded in complex environments. So a support vector machine is presented that can determine a trajectory for an exploration vehicle through unknown...
A modified version of Sugeno-Yasukawa (SY) modelling algorithm is presented. We have employed a new method for parameter identification phase based on genetic algorithms (GA). Moreover, we have modified the modelling sequence by applying parameter identification on intermediate models. Models created with this method had lower mean square errors (MSE) compared to original algorithm. A case study on...
This paper proposes an intelligent trading system using support vector regression optimized by genetic algorithms (SVR-GA) and multilayer perceptron optimized with GA (MLP-GA). Experimental results show that both approaches outperform conventional trading systems without prediction and a recent fuzzy trading system in terms of final equity and maximum drawdown for Hong Kong Hang Seng stock index.
Accurate forecasting of some economic indicators such as GDP is very useful. Aiming at the problem of modeling and forecasting of the nonlinear and complex economic system, an improved least square support machine model is proposed in this paper. A multi-scale chaotic search algorithm combined with GA is proposed for the optimum selection of model parameters. Time series data of the indicator to be...
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