Quantitative analysis of enterprise chain risk based on SVM algorithm and mathematical fuzzy set

2020 ◽  
Vol 39 (4) ◽  
pp. 5773-5783
Author(s):  
Gebing Sun
Keyword(s):  
Support Vector Machine ◽  
Supply Chain ◽  
Simulated Annealing ◽  
Quantitative Analysis ◽  
Fuzzy Set ◽  
Evaluation Model ◽  
Market Competition ◽  
Normal Operation ◽  
Support Vector ◽  
Svm Algorithm

Under the guidance and practice of lean production, just in time production and other advanced theories, the relationship between enterprises is becoming more and more closely. In order to cope with more fierce market competition, manufacturing enterprises began to strengthen cooperation with partners in the supply chain, gather resources, improve competitiveness and jointly fight against competitors. In these decades, the competition among enterprises is gradually replaced by the competition among supply chains. In this paper, the author makes quantitative analysis of enterprise chain risk based on SVM algorithm and mathematical fuzzy set. Support vector machine (SVM) is a machine learning method, has strong generalization ability and accuracy. By analyzing dexterity affects the normal operation of the supply chain risk factors, we use simulated annealing –mathematical fuzzy of the risk evaluation, it indicates that the model in risk assessment is applicable through empirical research. According to the data obtained, the simulated annealing –support vector machine evaluation model were trained and tested; the explanation on the choice of kernel function of the process of construction of the evaluation model, the parameters of the model to determine some key problems.

Intelligent Evaluation Model for Cementing Quality Based on PSO-SVM and Application

2011 ◽  
Vol 71-78 ◽  
pp. 4293-4299 ◽  
Author(s):  
Jing Cheng Liu ◽  
Hong Tu Wang ◽  
Shun Peng Zeng ◽  
Zhi Gang Yuan
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
High Speed ◽  
Evaluation Model ◽  
Research Result ◽  
Particle Swarm ◽  
Normal Operation ◽  
Support Vector ◽  
Swarm Optimization ◽  
Gas Well

The cementing quality is directly related to the normal operation of the gas well, therefore, the evaluation of cementing quality is key to the correctly use the gas well as well as to take measures to protect the gas well. In this paper, four first wave amplitudes at the same depth point when using the borehole compensated sonic logger with double transceiver technique to carry out the acoustic amplitude log operation are served as the discriminant factors to evaluate the cementing quality. Taking the engineering actual measured data as the learning samples and using the particle swarm optimization to optimize the parameters of support vector machine, this paper established the intelligent evaluation model for cementing quality based on particle swarm optimization (PSO) and support vector machine (SVM). The model employs the excellent characteristic of SVM which has high speed of solving and could describe nonlinear relation as well as the characteristic of PSO which has global optimization. Through test of engineering samples, the research result showed that this model has fast astringency and high precision, providing a new method and approach for the fast and accurate evaluation of the well cementing quality.

Intelligent Evaluation Model for Cementing Quality Based on GA-SVM and Application

2011 ◽  
Vol 121-126 ◽  
pp. 2730-2734 ◽  
Author(s):  
Jing Cheng Liu ◽  
Shun Peng Zeng ◽  
Zhi Gang Yuan
Keyword(s):  
Support Vector Machine ◽  
High Speed ◽  
Evaluation Model ◽  
Research Result ◽  
Measured Data ◽  
Normal Operation ◽  
Support Vector ◽  
Swarm Optimization ◽  
Gas Well ◽  
Nonlinear Relation

The cementing quality is directly related to the normal operation of the gas well, therefore, the evaluation of cementing quality is key to the correctly use the gas well as well as to take measures to protect the gas well. In this paper, four first wave amplitudes at the same depth point when using the borehole compensated sonic logger with double transceiver technique to carry out the acoustic amplitude log operation are served as the discriminant factors to evaluate the cementing quality. Taking the engineering actual measured data as the learning samples and using the particle swarm optimization to optimize the parameters of support vector machine, this paper established the intelligent evaluation model for cementing quality based on genetic algorithm (GA) and support vector machine (SVM). The model employs the excellent characteristic of SVM which has high speed of solving and could describe nonlinear relation as well as the characteristic of GA which has global optimization. Through test of engineering samples, the research result showed that this model has fast astringency and high precision, providing a new method and approach for the fast and accurate evaluation of the well cementing quality.

Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services

2020 ◽  
Vol 4 (2) ◽  
pp. 362-369
Author(s):  
Sharazita Dyah Anggita ◽  
Ikmah
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
The Public ◽  
Svm Algorithm ◽  
Bayes Algorithm ◽  
Freight Forwarding ◽  
Improved Accuracy

The needs of the community for freight forwarding are now starting to increase with the marketplace. User opinion about freight forwarding services is currently carried out by the public through many things one of them is social media Twitter. By sentiment analysis, the tendency of an opinion will be able to be seen whether it has a positive or negative tendency. The methods that can be applied to sentiment analysis are the Naive Bayes Algorithm and Support Vector Machine (SVM). This research will implement the two algorithms that are optimized using the PSO algorithms in sentiment analysis. Testing will be done by setting parameters on the PSO in each classifier algorithm. The results of the research that have been done can produce an increase in the accreditation of 15.11% on the optimization of the PSO-based Naive Bayes algorithm. Improved accuracy on the PSO-based SVM algorithm worth 1.74% in the sigmoid kernel.

scholarly journals Pulmonary Nodule Recognition Based on Multiple Kernel Learning Support Vector Machine-PSO

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yang Li ◽  
Zhichuan Zhu ◽  
Alin Hou ◽  
Qingdong Zhao ◽  
Liwei Liu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Pulmonary Nodule ◽  
Multiple Kernel Learning ◽  
Pso Algorithm ◽  
Support Vector ◽  
Training Time ◽  
Inertia Weight ◽  
Fitness Value ◽  
Svm Algorithm ◽  
Fitness Curve

Pulmonary nodule recognition is the core module of lung CAD. The Support Vector Machine (SVM) algorithm has been widely used in pulmonary nodule recognition, and the algorithm of Multiple Kernel Learning Support Vector Machine (MKL-SVM) has achieved good results therein. Based on grid search, however, the MKL-SVM algorithm needs long optimization time in course of parameter optimization; also its identification accuracy depends on the fineness of grid. In the paper, swarm intelligence is introduced and the Particle Swarm Optimization (PSO) is combined with MKL-SVM algorithm to be MKL-SVM-PSO algorithm so as to realize global optimization of parameters rapidly. In order to obtain the global optimal solution, different inertia weights such as constant inertia weight, linear inertia weight, and nonlinear inertia weight are applied to pulmonary nodules recognition. The experimental results show that the model training time of the proposed MKL-SVM-PSO algorithm is only 1/7 of the training time of the MKL-SVM grid search algorithm, achieving better recognition effect. Moreover, Euclidean norm of normalized error vector is proposed to measure the proximity between the average fitness curve and the optimal fitness curve after convergence. Through statistical analysis of the average of 20 times operation results with different inertial weights, it can be seen that the dynamic inertial weight is superior to the constant inertia weight in the MKL-SVM-PSO algorithm. In the dynamic inertial weight algorithm, the parameter optimization time of nonlinear inertia weight is shorter; the average fitness value after convergence is much closer to the optimal fitness value, which is better than the linear inertial weight. Besides, a better nonlinear inertial weight is verified.

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