scholarly journals Implementation of cuckoo search algorithm for support vector machine parameters optimization in pre collision warning

2020 ◽  
Vol 850 ◽  
pp. 012027
Author(s):  
A Puspaningrum ◽  
A Suheryadi ◽  
A Sumarudin
Keyword(s):  
Support Vector Machine ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Parameters Optimization ◽  
Support Vector ◽  
Collision Warning

scholarly journals Stability Evaluation of Rock Slope in Hydraulic Engineering Based on Improved Support Vector Machine Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Fei Li ◽  
Hongyun Zhang
Keyword(s):  
Support Vector Machine ◽  
Stability Analysis ◽  
Permeability Coefficient ◽  
Engineering Geology ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Support Vector ◽  
Svm Algorithm ◽  
Improved Support Vector Machine

The safety problem of the slope has always been an important subject in engineering geology, which has a wide range of application background and practical significance in reality. How to correctly evaluate the stability of the slope and obtain the parameters of the slope has always been the focus of research and production personnel at home and abroad. In recent years, various artificial intelligence calculation methods have been applied to the field of rock engineering and engineering geology, providing some new ideas for the solution of slope stability analysis and parameter back analysis. Support vector machine (SVM) algorithm has unique advantages and generalization in dealing with finite samples and highly complex and nonlinear problems. At present, it has become a research hotspot of intelligent methods and has been widely paid attention to in various application fields of slope engineering. In this paper, a cuckoo search algorithm-improved support vector machine (CS-SVM) method is applied to slope stability analysis and parameter inversion. Aiming at the problem of selecting kernel function parameters and penalty number of SVM, a method of using cuckoo search algorithm to improve support vector machine was proposed, and the global optimization ability of cuckoo search algorithm was used to improve the algorithm. Aiming at the slope samples collected, the classification algorithm of support vector machine (SVM) was used to identify the stable state of the test samples, and the improved SVM algorithm was used to analyze the safety factor of the test samples. The results show that the proposed method is reasonable and reliable. Based on the inversion of the permeability coefficient of the test samples by the improved support vector machine, the comparison between the inversion value and the theoretical value shows that it is basically feasible to invert the permeability coefficient of the dam slope by the improved support vector machine.

scholarly journals Mechanical Fault Diagnosis of an On-Load Tap Changer by Applying Cuckoo Search Algorithm-Based Fuzzy Weighted Least Squares Support Vector Machine

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Shuguo Gao ◽  
Cong Zhou ◽  
Zhigang Zhang ◽  
Jianghai Geng ◽  
Ruidong He ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Search Algorithm ◽  
Weighted Least Squares ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Support Vector ◽  
Mechanical Faults ◽  
Diagnosis Method ◽  
Tap Changer ◽  
Load Tap Changer

To improve adaptability, feature resolution, and identification accuracy when diagnosing mechanical faults in an on-load tap changer (OLTC) of a transformer, in the present research, wavelet packet energy entropy is used to describe the information comprising vibration signal in the switch process of an OLTC, and a fuzzy weighted least squares support vector machine (CSA-fuzzy weighted LSSVM) model based on the cuckoo search algorithm is proposed to identify mechanical fault types. Specifically, according to the different importance of the sample data in different periods, the idea of fuzzy weighting of training samples is proposed. The cuckoo search algorithm is used to optimise regularisation parameters, kernel function width, and weight control factor of CSA-fuzzy weighted LSSVM. Finally, the real experimental platform for typical mechanical faults of an OLTC is established, and the vibration signals of several typical mechanical faults under different degrees of fatigue are obtained. The results show that the new method achieves a higher accuracy rate of fault identification compared with other common methods. It can better deal with small sample and nonlinear prediction problems and shows higher fitting accuracy than CSA-LSSVM, single LSSVM, and radial basis neural network methods and is thus better suited for mechanical fault diagnosis in OLTCs. This paper presents a new intelligent diagnosis scheme for mechanical faults of on-load tap changers, which can achieve noninterruption and nonintrusive detection. The proposed diagnosis method would change the traditional diagnosis method of the on-load tap changer and improves the power supply quality and the detection efficiency under the premise of ensuring the safety of the staff.

scholarly journals Application of Credit Card Fraud Detection Based on CS-SVM

2021 ◽  
Vol 11 (1) ◽  
pp. 34-39
Author(s):  
Chenglong Li ◽  
◽  
Ning Ding ◽  
Haoyun Dong ◽  
Yiming Zhai ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Credit Card ◽  
Naive Bayes ◽  
Search Algorithm ◽  
Fraud Detection ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Support Vector ◽  
Credit Card Fraud

With the development of e-commerce, credit card fraud is also increasing. At the same time, the way of credit card fraud is also constantly innovating. Support Vector Machine, Logical Regression, Random Forest, Naive Bayes and other algorithms are often used in credit card fraud identification. However, the current fraud detection technology is not accurate, and may cause significant economic losses to cardholders and banks. This paper will introduce an innovative method to optimize the support vector machine by cuckoo search algorithm to improve its ability of identifying credit card fraud. Cuckoo search algorithm improves classification performance by optimizing the parameters of support vector machine kernel function (C, g). The results demonstrate that CS-SVM is superior to SVM in Accuracy, Precision, Recall, F1-score, AUC, and superior to Logistic. Regression, Random Forest, Decision Tree, Naive Bayes, whose accuracy is 98%.

scholarly journals A New Hybrid Approach for Wind Speed Forecasting Applying Support Vector Machine with Ensemble Empirical Mode Decomposition and Cuckoo Search Algorithm

2018 ◽  
Vol 8 (10) ◽  
pp. 1754 ◽  
Author(s):  
Tongxiang Liu ◽  
Shenzhong Liu ◽  
Jiani Heng ◽  
Yuyang Gao
Keyword(s):  
Wind Speed ◽  
Search Algorithm ◽  
Wind Farms ◽  
Cuckoo Search ◽  
Ensemble Empirical Mode Decomposition ◽  
Cuckoo Search Algorithm ◽  
Support Vector ◽  
Wind Speed Forecasting ◽  
Mode Decomposition ◽  
Proposed Model

Wind speed forecasting plays a crucial role in improving the efficiency of wind farms, and increases the competitive advantage of wind power in the global electricity market. Many forecasting models have been proposed, aiming to enhance the forecast performance. However, some traditional models used in our experiment have the drawback of ignoring the importance of data preprocessing and the necessity of parameter optimization, which often results in poor forecasting performance. Therefore, in order to achieve a more satisfying performance in forecasting wind speed data, a new short-term wind speed forecasting method which consists of Ensemble Empirical Mode Decomposition (EEMD) for data preprocessing, and the Support Vector Machine (SVM)—whose key parameters are optimized by the Cuckoo Search Algorithm (CSO)—is developed in this paper. This method avoids the shortcomings of some traditional models and effectively enhances the forecasting ability. To test the prediction ability of the proposed model, 10 min wind speed data from wind farms in Shandong Province, China, are used for conducting experiments. The experimental results indicate that the proposed model cannot only improve the forecasting accuracy, but can also be an effective tool in assisting the management of wind power plants.

scholarly journals Dual-Mass MEMS Gyroscope Parallel Denoising and Temperature Compensation Processing Based on WLMP and CS-SVR

Micromachines ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 586 ◽  
Author(s):  
Longkang Chang ◽  
Huiliang Cao ◽  
Chong Shen
Keyword(s):  
Wavelet Transform ◽  
Support Vector Regression ◽  
Temperature Compensation ◽  
Linear Prediction ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Temperature Error ◽  
Support Vector ◽  
Mixed Features

For the sake of decreasing the effects of noise and temperature error on the measurement accuracy of micro-electro-mechanical system (MEMS) gyroscopes, a denoising and temperature drift compensation parallel model method based on wavelet transform and forward linear prediction (WFLP) and support vector regression based on the cuckoo search algorithm (CS-SVR) is proposed in this paper. First, variational mode decomposition (VMD) is proposed in this paper, which is aimed at dividing the output signal of the gyroscope into intrinsic mode functions (IMFs); then, the IMFs are classified into three features—drift, mixed, and pure noise features—by the sample entropy (SE) value. Second, a wavelet transform and forward linear prediction (WFLP) are combined to remove the noise from the mixed features. Meanwhile, the drift feature is compensated by support vector regression based on the cuckoo search algorithm (CS-SVR). Finally, through reconstruction, the final signal is obtained. Experimental results demonstrate that the VMD-SE-WFLP-CS-SVR method proposed in this paper can decrease noise and compensate the temperature error effectively (angular random walking value is optimized from 1.667°/√h to 0.0667°/√h and the bias stability is reduced from 30°/h to 4°/h). In terms of denoising, the performance of the WFLP algorithm is superior to the wavelet threshold and FLP, as it combines their advantages; furthermore, in terms of temperature compensation, the proposed CS-SVR algorithm uses the cuckoo search algorithm to find the optimal parameters of SVR, improving the accuracy of the model.

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