Integrated parameter inversion analysis method of a CFRD based on multi-output support vector machines and the clonal selection algorithm

2013 ◽  
Vol 47 ◽  
pp. 68-77 ◽  
Author(s):  
Dongjian Zheng ◽  
Lin Cheng ◽  
Tengfei Bao ◽  
Beibei Lv
Keyword(s):  
Support Vector Machines ◽  
Clonal Selection ◽  
Support Vector ◽  
Clonal Selection Algorithm ◽  
Analysis Method ◽  
Selection Algorithm ◽  
Parameter Inversion ◽  
Vector Machines ◽  
Inversion Analysis

A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant

2014 ◽  
Vol 71 (4) ◽  
pp. 524-528 ◽  
Author(s):  
Ting Sie Chun ◽  
M. A. Malek ◽  
Amelia Ritahani Ismail
Keyword(s):  
Model Simulation ◽  
Clonal Selection ◽  
Treatment Plant ◽  
Model Performance ◽  
Oxygen Demand ◽  
Least Square ◽  
Support Vector ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Sludge Treatment

The development of effluent removal prediction is crucial in providing a planning tool necessary for the future development and the construction of a septic sludge treatment plant (SSTP), especially in the developing countries. In order to investigate the expected functionality of the required standard, the prediction of the effluent quality, namely biological oxygen demand, chemical oxygen demand and total suspended solid of an SSTP was modelled using an artificial intelligence approach. In this paper, we adopt the clonal selection algorithm (CSA) to set up a prediction model, with a well-established method – namely the least-square support vector machine (LS-SVM) as a baseline model. The test results of the case study showed that the prediction of the CSA-based SSTP model worked well and provided model performance as satisfactory as the LS-SVM model. The CSA approach shows that fewer control and training parameters are required for model simulation as compared with the LS-SVM approach. The ability of a CSA approach in resolving limited data samples, non-linear sample function and multidimensional pattern recognition makes it a powerful tool in modelling the prediction of effluent removals in an SSTP.

A novel bevel gear fault diagnosis method based on ensemble empirical mode decomposition and support vector machines

2020 ◽  
Vol 62 (1) ◽  
pp. 34-41
Author(s):  
Sun Yanqiang ◽  
Chen Hongfang ◽  
Shi Zhaoyao ◽  
Tang Liang
Keyword(s):  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Bevel Gear ◽  
Support Vector ◽  
Analysis Method ◽  
Mode Decomposition ◽  
Signal Characteristics ◽  
Vector Machines

A novel analysis method is proposed based on ensemble empirical mode decomposition (EEMD) and support vector machines (SVMs) for the fault diagnosis of bevel gears. Firstly, the EEMD method is used to decompose the fluctuations in the original gear noise signals into different timescales so as to obtain several intrinsic mode functions (IMFs). The meshing frequency components in the decomposition results are reconstructed to eliminate the influence of interference noise. Then, time-synchronous averaging (TSA) is applied in further denoising to weaken signals independent of the gear meshing frequency. After denoising, various signal characteristics are calculated. Obvious signal characteristics for different fault states are selected as a set of feature vectors. Finally, a particle optimisation method is used to optimise SVM parameters and the feature vectors are input as training samples into an SVM in order to achieve fault recognition. The experimental results show that this novel analysis method can effectively diagnose different conditions of the bevel gear and achieve an identification rate for gear faults of 98.33%.

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