scholarly journals Cell Group Recognition Method Based on Adaptive Mutation PSO-SVM

Cells ◽  
2018 ◽  
Vol 7 (9) ◽  
pp. 135 ◽  
Author(s):  
Yue Wang ◽  
Xiaochen Meng ◽  
Lianqing Zhu
Keyword(s):  
Group Identification ◽  
Pso Algorithm ◽  
Multidimensional Data ◽  
Cell Group ◽  
Adaptive Mutation ◽  
Support Vector ◽  
Penalty Factor ◽  
Kernel Parameter ◽  
Svm Model ◽  
Factor C

The increased volume and complexity of flow cytometry (FCM) data resulting from the increased throughput greatly boosts the demand for reliable statistical methods for the analysis of multidimensional data. The Support Vector Machines (SVM) model can be used for classification recognition. However, the selection of penalty factor c and kernel parameter g in the model has a great influence on the correctness of clustering. To solve the problem of parameter optimization of the SVM model, a support vector machine algorithm of particle swarm optimization (PSO-SVM) based on adaptive mutation is proposed. Firstly, a large number of FCM data were used to carry out the experiment, and the kernel function adapted to the sample data was selected. Then the PSO algorithm of adaptive mutation was used to optimize the parameters of the SVM classifier. Finally, the cell clustering results were obtained. The method greatly improves the clustering correctness of traditional SVM. That also overcomes the shortcomings of PSO algorithm, which is easy to fall into local optimum in the iterative optimization process and has poor convergence effect in dealing with a large number of data. Compared with the traditional SVM algorithm, the experimental results show that, the correctness of the method is improved by 19.38%. Compared with the cross-validation algorithm and the PSO algorithm, the adaptive mutation PSO algorithm can also improve the correctness of FCM data clustering. The correctness of the algorithm can reach 99.79% and the time complexity is relatively lower. At the same time, the method does not need manual intervention, which promotes the research of cell group identification in biomedical detection technology.

scholarly journals Bearing Fault Diagnosis Using a Support Vector Machine Optimized by an Improved Ant Lion Optimizer

2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Dalian Yang ◽  
Jingjing Miao ◽  
Fanyu Zhang ◽  
Jie Tao ◽  
Guangbin Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Model Parameters ◽  
Bearing Faults ◽  
Ring Fault ◽  
Penalty Factor ◽  
Kernel Parameter ◽  
Svm Model

Bearing is an important mechanical component that easily fails in a bad working environment. Support vector machines can be used to diagnose bearing faults; however, the recognition ability of the model is greatly affected by the kernel function and its parameters. Unfortunately, optimal parameters are difficult to select. To address these limitations, an escape mechanism and adaptive convergence conditions were introduced to the ALO algorithm. As a result, the EALO method was proposed and has been applied to the more accurate selection of SVM model parameters. To assess the model, the vibration acceleration signals of normal, inner ring fault, outer ring fault, and ball fault bearings were collected at different rotation speeds (1500 r/min, 1800 r/min, 2100 r/min, and 2400 r/min). The vibration signals were decomposed using the variational mode decomposition (VMD) method. The features were extracted through the kernel function to fuse the energy value of each VMD component. In these experiments, the two most important parameters for the support vector machine—the Gaussian kernel parameter σ and the penalty factor C—were optimized using the EALO algorithm, ALO algorithm, genetic algorithm (GA), and particle swarm optimization (PSO) algorithm. The performance of these four methods to optimize the two parameters was then compared and analyzed, with the EALO method having the best performance. The recognition rates for bearing faults under different tested rotation speeds were improved when the SVM model parameters optimized by the EALO were used.

Fault Diagnosis of Feedwater Pump in Nuclear Power Plants Using Parameter-Optimized Support Vector Machine

2016 ◽  
Author(s):  
Rui Yu ◽  
Xianling Li ◽  
Mo Tao ◽  
Zhiwu Ke
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Nuclear Power ◽  
Power Plants ◽  
Superior Performance ◽  
Support Vector ◽  
Feedwater Pump ◽  
Penalty Factor ◽  
Kernel Parameter ◽  
Factor C

The condition monitoring of the feedwater pump in secondary circuit is critical to the safe operation of the nuclear power plant. This article presents a fault diagnosis method of feedwater pump by using parameter-optimized support vector machine (SVM). While the fault features of feedwater pump are reflected from the power spectrum of the vibration signals, we trained and diagnosed the fault feature table with support vector machine. The optimal penalty factor C and kernel parameter γ of support vector machine are selected by grid search and k-fold cross validation. Then the faults are diagnosed by the SVM model under the optimal parameters. Diagnostic results show that the parameter-optimized SVM method achieves higher diagnostic accuracy than the PNN method, exhibiting superior performance to effectively diagnose the faults of feedwater pump.

scholarly journals Modeling and Parameter Identification of the MR Damper Based on LS-SVM

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Cheng Qian ◽  
Xiaoliang Yin ◽  
Qing Ouyang
Keyword(s):  
Genetic Algorithm ◽  
Mr Damper ◽  
Small Sample ◽  
Least Square ◽  
Average Error ◽  
Support Vector ◽  
Current Output ◽  
Penalty Factor ◽  
Kernel Parameter ◽  
Svm Model

In order to identify the nonlinear characteristics of the magnetorheological (MR) damper applied in multi-DOF vibration reduction platforms in the aerospace field in the modeling process, the least square support vector machine (LS-SVM) method is adopted, because LS-SVM can handle small-sample, high-dimensional characteristic problems. Firstly, the theory of the modeling method based on LS-SVM was illustrated including the genetic algorithm (GA) optimization method. Secondly, the characteristic curve of the MR damper was tested based on different conditions. Then, the current and historical input displacement, velocity, and current and the historical output are taken as the input of the LS-SVM model and the damping force of the current output is taken as the output of the model for model training. Meanwhile, the genetic algorithm is introduced to optimize the parameters of the LS-SVM model which affect the accuracy of the model, the penalty factor c = 16.48 , and the kernel parameter σ = 3.39 after optimization. Finally, in order to verify the method adopted in the paper, the Simulink model was simulated in certain input conditions; by comparing the simulation and experimental values of this model, it is found that the maximum error is within 10 N and the average error is around 0.89 N, which is similar to the accuracy obtained in other works of literature, and the correctness of this model is verified.

scholarly journals Parameter-identification investigations on the hysteretic Preisach model improved by the fuzzy least square support vector machine based on adaptive variable chaos immune algorithm

2017 ◽  
Vol 36 (3) ◽  
pp. 227-242 ◽  
Author(s):  
E Jiaqiang ◽  
Cheng Qian ◽  
Hao Zhu ◽  
Qingguo Peng ◽  
Wei Zuo ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Immune Algorithm ◽  
Least Square ◽  
Support Vector ◽  
Preisach Model ◽  
Model Based ◽  
Weight Factors ◽  
Penalty Factor ◽  
Kernel Parameter ◽  
Factor C

In order to solve the hysteretic character of the piezoelectric material for application, the initial weight factors of the hysteretic units are calculated by the Preisach theory and the first-order reversal curves test data, a hysteretic Preisach model based on the improved fuzzy least square support vector machine (improved FLS-SVM) is established. In the established model, the fuzzy least square support vector machine is introduced to calculate more weight factors of the hysteretic units and the adaptive variable chaos immune algorithm is introduced to optimize the penalty factor and the kernel parameter of the FLS-SVM (the penalty factor c = 35 and the kernel parameter σ = 1.35 are obtained). Moreover, the quadratic polynomial interpolation method is used to eliminate the sawtooth phenomenon. The validity of established model reveals that fuzzy least square support vector machine method based on adaptive variable chaos immune algorithm (FLS-SVMAVCIA) is more accurate than FLS-SVM method according to application results of the real actuators (the absolute mean error of the FLS-SVMAVCIA model is less than 1 µm and its maximum error is less than 2 µm). As a result, the hysteretic phenomenon can be effectively eliminated by the hysteretic Preisach model based on the FLS-SVMAVCIA method.

scholarly journals Intelligent Fault Identification for Rolling Bearings Fusing Average Refined Composite Multiscale Dispersion Entropy-Assisted Feature Extraction and SVM with Multi-Strategy Enhanced Swarm Optimization

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 527
Author(s):  
Huibin Shi ◽  
Wenlong Fu ◽  
Bailin Li ◽  
Kaixuan Shao ◽  
Duanhao Yang
Keyword(s):  
Feature Extraction ◽  
Normal Operation ◽  
Fault Identification ◽  
Support Vector ◽  
Mechanical Equipment ◽  
Rolling Bearings ◽  
Swarm Optimization ◽  
Penalty Factor ◽  
Kernel Parameter ◽  
Factor C

Rolling bearings act as key parts in many items of mechanical equipment and any abnormality will affect the normal operation of the entire apparatus. To diagnose the faults of rolling bearings effectively, a novel fault identification method is proposed by merging variational mode decomposition (VMD), average refined composite multiscale dispersion entropy (ARCMDE) and support vector machine (SVM) optimized by multistrategy enhanced swarm optimization in this paper. Firstly, the vibration signals are decomposed into different series of intrinsic mode functions (IMFs) based on VMD with the center frequency observation method. Subsequently, the proposed ARCMDE, fusing the superiorities of DE and average refined composite multiscale procedure, is employed to enhance the ability of the multiscale fault-feature extraction from the IMFs. Afterwards, grey wolf optimization (GWO), enhanced by multistrategy including levy flight, cosine factor and polynomial mutation strategies (LCPGWO), is proposed to optimize the penalty factor C and kernel parameter g of SVM. Then, the optimized SVM model is trained to identify the fault type of samples based on features extracted by ARCMDE. Finally, the application experiment and contrastive analysis verify the effectiveness of the proposed VMD-ARCMDE-LCPGWO-SVM method.

scholarly journals Application of SVM Optimized by IPSO in Rolling Bearing Fault Diagnosis

2018 ◽  
Vol 227 ◽  
pp. 02007
Author(s):  
Mingzhu Lv ◽  
Xiaoming Su ◽  
Shixun Liu
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Neighborhood Search ◽  
Support Vector ◽  
Svm Classifier ◽  
Classification Problems ◽  
Penalty Factor ◽  
Kernel Parameter ◽  
Effect Of Support ◽  
Factor C

Aiming at the problem that the classification effect of support vector machine (SVM) is not satisfactory due to improper selection of penalty factor C and kernel parameter g, this paper proposes a new modified classifier that uses the improved particle swarm optimization (IPSO) to optimize the parameter of SVM (IPSO-SVM) by introducing the dynamic inertia weight, global neighborhood search, population shrinkage factor and particle mutation probability. The classification result is tested by Common data sets named BreastTissue、 Heart and Wine from the Libsvm toolbox, the results show that IPSO-SVM classifier is obviously superior to SVM and PSO-SVM classifier in terms of prediction accuracy and classification time. Then it is applied to the fault diagnosis in two classification problems and multiple classification problems of rolling bearings. The simulation results show that the IPSO-SVM classifier has stronger global convergence ability and faster convergence speed, and the ideal classification results can be obtained. Finally, the IPSO-SVM classifier is used to diagnose the fault of the actual bearing. The results show that the classifier has a better classification stability and is worthy of further promotion in engineering field.

scholarly journals Gas Outburst Prediction Model Using Improved Entropy Weight Grey Correlation Analysis and IPSO-LSSVM

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Haibo Liu ◽  
Yujie Dong ◽  
Fuzhong Wang
Keyword(s):  
Correlation Analysis ◽  
Prediction Model ◽  
Least Square ◽  
Support Vector ◽  
Entropy Weight ◽  
Grey Correlation ◽  
Gas Outburst ◽  
Penalty Factor ◽  
Kernel Parameter ◽  
Grey Correlation Analysis

This paper investigates the problem of gas outburst prediction in the working face of coal mine. Firstly, based on a comprehensive analysis of influence factors of gas outburst, an improved entropy weight algorithm is introduced into a grey correlation analysis algorithm; thus, the reasonable weights and correlation order of the influencing factors are obtained to improve the objectivity of the evaluation. The main controlling factors obtained are used as the input of the prediction model. Secondly, by utilizing the improved particle swarm optimization (IPSO), the penalty factor and kernel parameter of least square support vector machine (LSSVM) are optimized to enhance the global search ability and avoid the occurrence of the local optimal solutions, and a new prediction model of gas outburst based on IPSO-LSSVM is established. At last, the prediction model is applied in the tunneling heading face 14141 of Jiuli Hill mine in Jiaozuo City, China. The case study demonstrates that the prediction accuracy of the proposed model is 92%, which is improved compared with that of the SVM model and GA-LSSVM model.

scholarly journals Heating load prediction based on particle swarm optimization support vector machine

2021 ◽  
Vol 2087 (1) ◽  
pp. 012058
Author(s):  
Xiuchao Chen ◽  
Shenghui Wang ◽  
Xing Jin
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Training Model ◽  
Function Parameter ◽  
Support Vector ◽  
Load Prediction ◽  
Swarm Optimization ◽  
Penalty Factor ◽  
Heating Load ◽  
Factor C

Abstract Heating load is affected by many uncertain factors, which makes it show certain randomness. To further improve the heating load forecasting accuracy, reduce the prediction error, using cross validation (CV) ideology in the choice of a model of performance evaluation and the superiority, combined with the advantages of particle swarm optimization (PSO), which is easy to implement and has stronger global optimization ability, the important parameters (penalty factor C and RBF kernel function parameter γ) are optimized, and the best parameters are automatically found in the training set, so as to obtain the best training model. Compared with other algorithms, the model precision of this method is improved a lot, and the prediction result is more accurate.

Nonlinearity Compensation for Thermocouple Vacuum Gauge Using Particle Swarm Optimized Least Squares Support Vector Machine

2013 ◽  
Vol 321-324 ◽  
pp. 2177-2182
Author(s):  
Yao Geng Tang ◽  
Song Gao ◽  
Xing Qu
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Support Vector ◽  
Vacuum Gauge ◽  
Swarm Optimization ◽  
Svm Model ◽  
Using Data ◽  
Nonlinearity Characteristic

A method for compensating nonlinear characteristic of thermocouple vacuum gauge is proposed. Least squares support vector machine (LS-SVM) is adopt as compensation model, of which parameters are optimized using particle swarm optimization (PSO) algorithm. Experimental results using data obtained on-site show that the proposed approach effectively compensates the nonlinearity characteristic, and the accuracy of this method is higher than those obtained by SVM model.

scholarly journals Nonlinear Inertia Classification Model and Application

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Mei Wang ◽  
Pai Wang ◽  
Jzau-Sheng Lin ◽  
Xiaowei Li ◽  
Xuebin Qin
Keyword(s):  
Search Algorithm ◽  
Great Influence ◽  
Nonlinear Function ◽  
Classification Model ◽  
Support Vector ◽  
Power Cable ◽  
Optimal Position ◽  
Kernel Parameter ◽  
Svm Model

Classification model of support vector machine (SVM) overcomes the problem of a big number of samples. But the kernel parameter and the punishment factor have great influence on the quality of SVM model. Particle swarm optimization (PSO) is an evolutionary search algorithm based on the swarm intelligence, which is suitable for parameter optimization. Accordingly, a nonlinear inertia convergence classification model (NICCM) is proposed after the nonlinear inertia convergence (NICPSO) is developed in this paper. The velocity of NICPSO is firstly defined as the weighted velocity of the inertia PSO, and the inertia factor is selected to be a nonlinear function. NICPSO is used to optimize the kernel parameter and a punishment factor of SVM. Then, NICCM classifier is trained by using the optical punishment factor and the optical kernel parameter that comes from the optimal particle. Finally, NICCM is applied to the classification of the normal state and fault states of online power cable. It is experimentally proved that the iteration number for the proposed NICPSO to reach the optimal position decreases from 15 to 5 compared with PSO; the training duration is decreased by 0.0052 s and the recognition precision is increased by 4.12% compared with SVM.

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