نتایج جستجو برای: least squares support vector machine lssvm
تعداد نتایج: 1376443 فیلتر نتایج به سال:
Stable night-time light data from the Defense Meteorological Satellite Program (DMSP) Operational Line-scan System (OLS) provide a unique proxy for anthropogenic development. This paper presents a regional urban extent extraction method using a one-class classifier and combinations of DMSP/OLS stable night-time light (NTL) data, MODIS normalized difference vegetation index (NDVI) data, and land...
Article history: Received 9 September 2014 Received in revised form 25 January 2015 Accepted 9 February 2015 Available online xxxx
a Information Technology Supporting Center, Institute of Scientific and Technical Information of China No. 15 Fuxing Rd., Haidian District, Beijing 100038, China b School of Economics and Management, Beijing Forestry University No. 35 Qinghua East Rd., Haidian District, Beijing 100038, China College of Information and Electrical Engineering, China Agricultural University No. 17 Qinghua East Rd....
In order to realize real-time and precise monitoring of the tool wear in milling process, this paper presents a predictive model based on stacked multilayer denoising autoencoders (SMDAE) technique, particle swarm optimization with an adaptive learning strategy (PSO-ALS), least squares support vector machine (LSSVM). Cutting force vibration information are adopted as signals. Three steps make u...
Predicting the price of electricity is crucial for operation power systems. Short-term forecasting deals with forecasts from an hour to a day ahead. Hourly-ahead offer expected prices market participants before hours. This especially useful effective bidding strategies where amount can be reviewed or changed Nevertheless, many existing models have relatively low prediction accuracy. Furthermore...
Accurate prediction of forthcoming oxygen concentration during waterless live fish transportation plays a key role in reducing the abnormal occurrence, increasing the survival rate in delivery operations, and optimizing manufacturing costs. The most effective ambient monitoring techniques that are based on the analysis of historical process data when performing forecasting operations do not ful...
This paper proposes an effective model based on the least squares support vector machines (LSSVM) and the particle swarm optimization (PSO), termed PSO-LSSVM, for prediction of natural gas consumption, as an important energy resource. The salient feature of mapping nonlinear data into high dimension feature space, distinguishes LS-SVM as a powerful approach for forecasting and estimation. Optim...
Short-term wind power forecasting plays an important role in generation systems. In order to improve the accuracy of forecasting, many researchers have proposed a large number models. However, traditional models ignore data preprocessing and limitations single model, resulting low accuracy. Aiming at shortcomings existing models, combined model based on secondary decomposition technique grey wo...
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