نتایج جستجو برای: least squares support vector machine lssvm

تعداد نتایج: 1376443  

Journal: :DEStech Transactions on Environment, Energy and Earth Sciences 2019

In this work, a Genetic Algorithm boosted Least Square Support Vector Machine model by a set of linear equations instead of a quadratic program, which is improved version of Support Vector Machine model, was used for estimation of 98 pure compounds second virial coefficient. Compounds were classified to the different groups. Finest parameters were obtained by Genetic Algorithm method ...

2015
Ying-Pei Liu Hai-Ping Liang Zhong-Ke Gao Xiaosong Hu

In order to improve the performance of voltage source converter-high voltage direct current (VSC-HVDC) system, we propose an improved auto-disturbance rejection control (ADRC) method based on least squares support vector machines (LSSVM) in the rectifier side. Firstly, we deduce the high frequency transient mathematical model of VSC-HVDC system. Then we investigate the ADRC and LSSVM principles...

Journal: :International Journal of Applied Mathematics and Computer Science 2011

2017
Jianhua Zhang Zhong Yin Rubin Wang

This paper developed a cognitive task-load (CTL) classification algorithm and allocation strategy to sustain the optimal operator CTL levels over time in safety-critical human-machine integrated systems. An adaptive human-machine system is designed based on a non-linear dynamic CTL classifier, which maps a set of electroencephalogram (EEG) and electrocardiogram (ECG) related features to a few C...

2014
Kun Zhang Minrui Fei Xin Li Huiyu Zhou

Features analysis is an important task which can significantly affect the performance of automatic bacteria colony picking. Unstructured environments also affect the automatic colony screening. This paper presents a novel approach for adaptive colony segmentation in unstructured environments by treating the detected peaks of intensity histograms as a morphological feature of images. In order to...

2007
Kai-Ling Mak D. H. Yang

show that the proposed method is an excellent forecasting tool for logistics management.

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