scholarly journals Icing Forecasting of High Voltage Transmission Line Using Weighted Least Square Support Vector Machine with Fireworks Algorithm for Feature Selection

2016 ◽  
Author(s):  
Tiannan Ma ◽  
Dongxiao Niu
Keyword(s):  
Support Vector Machine ◽  
Least Square ◽  
Support Vector ◽  
Data Set ◽  
Promising Alternative ◽  
Forecasting Accuracy ◽  
High Voltage Transmission Line ◽  
Fireworks Algorithm ◽  
Weighted Least Square ◽  
Forecasting System

Accurate forecasting of icing thickness has a great significance for ensuring the security and stability of power grid. In order to improve the forecasting accuracy, this paper proposes an icing forecasting system based on fireworks algorithm and weighted least square support vector machine (W-LSSVM). The method of fireworks algorithm is employed to select the proper input features with the purpose of eliminating the redundant influence. In addition, the aim of W-LSSVM model is to train and test the historical data-set with the selected features. The capability of this proposed icing forecasting model and framework is tested through the simulation experiments using real-world icing data from monitoring center of key laboratory of anti-ice disaster, Hunan, South China. The results show that the proposed W-LSSVM-FA method has a higher prediction accuracy and it may be a promising alternative for icing thickness forecasting.

scholarly journals Beam-hardening correction by a surface fitting and phase classification by a least square support vector machine approach for tomography images of geological samples

2015 ◽  
Vol 7 (4) ◽  
pp. 3383-3408 ◽  
Author(s):  
F. Khan ◽  
F. Enzmann ◽  
M. Kersten
Keyword(s):  
Support Vector Machine ◽  
Image Data ◽  
Least Square ◽  
Training Data ◽  
Support Vector ◽  
Beam Hardening ◽  
Reconstruction Procedure ◽  
Data Set ◽  
Corrected Image ◽  
Computed Microtomography

Abstract. In X-ray computed microtomography (μXCT) image processing is the most important operation prior to image analysis. Such processing mainly involves artefact reduction and image segmentation. We propose a new two-stage post-reconstruction procedure of an image of a geological rock core obtained by polychromatic cone-beam μXCT technology. In the first stage, the beam-hardening (BH) is removed applying a best-fit quadratic surface algorithm to a given image data set (reconstructed slice), which minimizes the BH offsets of the attenuation data points from that surface. The final BH-corrected image is extracted from the residual data, or the difference between the surface elevation values and the original grey-scale values. For the second stage, we propose using a least square support vector machine (a non-linear classifier algorithm) to segment the BH-corrected data as a pixel-based multi-classification task. A combination of the two approaches was used to classify a complex multi-mineral rock sample. The Matlab code for this approach is provided in the Appendix. A minor drawback is that the proposed segmentation algorithm may become computationally demanding in the case of a high dimensional training data set.

scholarly journals Research on the Algorithm System for Generation and Creation by Computer Artificial Intelligence and Support Vector Machine

2021 ◽  
Vol 2083 (4) ◽  
pp. 042039
Author(s):  
Xuan Wu
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Least Square ◽  
Support Vector ◽  
Generation Model ◽  
Algorithmic Composition ◽  
Data Set ◽  
Production Program ◽  
Music Production ◽  
Standard Support Vector Machine

Abstract Algorithmic composition is also called automated composition. It is an attempt to use a specific form of process. Composers make full use of computers to carry out music creation and reduce their access. In this paper, based on the standard support vector machine (SVM) learning neural network, the least square support vector machine (LS-SVM) is combined with the recurrent neural network, and a new least square support vector machine learning neural network is proposed. The article realizes the efficient end-to-end multi-dimensional sound wave time series generation model Music-coder, through which the music style music of the famous singer Jay Chou is generated, and the quantified similarity with the real Jay Chou music data set reaches a maximum of 97.73%. The project in this paper shows that intelligent algorithm as a composition tool for music generation and creation is an effective music production program and will bring new development to music production.

Time Series Forecasting using Least Square Support Vector Machine for Canadian Lynx Data

2014 ◽  
Vol 70 (5) ◽  
Author(s):  
Shuhaida Ismail ◽  
Ani Shabri
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Support Vector Machine ◽  
Moving Average ◽  
Absolute Error ◽  
Research Area ◽  
Time Series Forecasting ◽  
Least Square ◽  
Support Vector ◽  
Promising Alternative

Time series analysis and forecasting is an active research area over the last few decades. There are various kinds of forecasting models have been developed and researchers have relied on statistical techniques to predict the future. This paper discusses the application of Least Square Support Vector Machine (LSSVM) models for Canadian Lynx forecasting. The objective of this paper is to examine the flexibility of LSSVM in time series forecasting by comparing it with other models in previous research such as Artificial Neural Networks (ANN), Auto-Regressive Integrated Moving Average (ARIMA), Feed-Forward Neural Networks (FNN), Self-Exciting Threshold Auto-Regression (SETAR), Zhang’s model, Aladang’s hybrid model and Support Vector Regression (SVR) model. The experiment results show that the LSSVM model outperforms the other models based on the criteria of Mean Absolute Error (MAE) and Mean Square Error (MSE). It also indicates that LSSVM provides a promising alternative technique in time series forecasting.

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