A water quality prediction model based on variational mode decomposition and the least squares support vector machine optimized by the sparrow search algorithm (VMD-SSA-LSSVM) of the Yangtze River, China

2021 ◽  
Vol 193 (6) ◽  
Author(s):  
Chenguang Song ◽  
Leihua Yao ◽  
Chengya Hua ◽  
Qihang Ni
Keyword(s):  
Water Quality ◽  
Support Vector Machine ◽  
Yangtze River ◽  
Search Algorithm ◽  
Support Vector ◽  
Quality Prediction ◽  
The Yangtze River ◽  
Variational Mode Decomposition ◽  
Water Quality Prediction ◽  
Mode Decomposition

scholarly journals Crow Search Algorithm for MEMS Gyroscope Temperature Drift Signal and Processing for Denoising

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xichen Wang ◽  
Huiliang Cao ◽  
Xiaomin Duan
Keyword(s):  
Search Algorithm ◽  
Noise Signal ◽  
Sample Entropy ◽  
Least Square ◽  
Effect Of Temperature ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Temperature Drift ◽  
Mems Gyroscope ◽  
Mode Decomposition

To solve the problem of micro-electro-mechanical system (MEMS) gyroscope noise, this paper presents a variational mode decomposition (VMD) method based on crow search algorithm. First, the signal was decomposed by variational mode decomposition for optimization of crow search algorithm (CSA-VMD) method. The parameters required by the VMD method (penalty parameter α and decomposition number K) are given by the crow search algorithm, and then the signal is decomposed into the superposition of multiple subsignals, called intrinsic mode functions (IMFs). The sample entropy (SE) corresponding to each IMF is then obtained. By calculating the sample entropy, the noise signal can be divided into pure noise part, mixing part, and temperature drift part. Second, Savitzky–Golay smoothing denoising (SG) is used to filter the mixed noise signal to eliminate the influence of noise. Third, for the filtering of the drift part, the least square support vector machine optimized by the crow search algorithm (CSA-LSSVM) was used to filter, so as to reduce the effect of temperature drift. Finally, the processed signal is reconstructed to achieve the goal of denoising. Through the results, it can be found that the optimized VMD and LSSVM using CSA algorithm can achieve more effective denoising. After using the method proposed in this paper, the angular random walk value is 1.1175    ∗  10−4°/h/√Hz, and the bias stability is 0.0017°/h. Compared with the original signal, the two signals are optimized by 98.1% and 98.2%, respectively. It can be seen from the experimental results that the proposed CSA-VMD method, SG method, and CSA-LSSVM method can effectively eliminate noise effects.

scholarly journals A Novel Shearer Cutting State Recognition Method Based on Improved Variational Mode Decomposition and LSSVM with Acoustic Signals

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Zhongbin Wang ◽  
Bin Liang ◽  
Lei Si ◽  
Kuangwei Tong ◽  
Chao Tan
Keyword(s):  
Search Algorithm ◽  
Optimal Parameter ◽  
Gravitational Search Algorithm ◽  
Principal Component ◽  
Least Square ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Recognition Method ◽  
Mode Decomposition

The recognition of shearer cutting state is the key technology to realize the intelligent control of the shearer, which has become a highly difficult subject concerned by the world. This paper takes the sound signal as analytic objects and proposes a novel recognition method based on the combination of variational mode decomposition (VMD), principal component analysis method (PCA), and least square support vector machine (LSSVM). VMD can decompose a signal into various modes by using calculus of variation and effectively avoid the false component and mode mixing problems. On this basis, an improved gravitational search algorithm (IGSA) is designed by using the position update mechanism of Levy flight strategy to find the optimal parameter combination of VMD. Then, the feature extraction is achieved by calculating the envelope entropy and kurtosis of the decomposed intrinsic mode functions (IMFs). To avoid dimensional disasters and reinforce the classification performance, PCA is introduced to choose useful features, and the LSSVM-based classifier is reasonably constructed. Finally, the experimental results indicate that the proposed method is more feasible and superior in the recognition of shearer cutting states.

Water Quality Prediction Based on Grey-Support Vector Regression

2012 ◽  
Vol 433-440 ◽  
pp. 263-267
Author(s):  
Jing Du ◽  
Tao Tao
Keyword(s):  
Water Quality ◽  
Support Vector Regression ◽  
Hybrid Method ◽  
Development Trend ◽  
Grey Model ◽  
Support Vector ◽  
Quality Prediction ◽  
Water Quality Prediction ◽  
Input And Output ◽  
Nerual Network

Water quality prediction has a great significance to evalute the quality development trend of water and make the planning of water processing. In the study, a novel hybrid method of grey model and support vector regression is proposed to improve the prediction accuracy of sypport vector regression. COD is the important composition in the polluting water. Thus, COD is used as evaluation index of polluting water in the paper. The quality prediction values of input and output water of BP nerual network and the hybrid method of grey model and support vector regression are computated respectively. It is indicated that the water quality prediction by using the hybrid method of grey model and support vector regression is superior to BP nerual network.

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