Support Vector Machine Theory Based Shift Quality Assessment for Automated Mechanical Transmission (AMT)

2007 ◽  
Author(s):  
Wang Jian ◽  
Guo Konghui ◽  
Lei Yulong ◽  
Tian Hua
Keyword(s):  
Support Vector Machine ◽  
Quality Assessment ◽  
Support Vector ◽  
Mechanical Transmission ◽  
Shift Quality ◽  
Machine Theory

scholarly journals Model Quality Assessment Method Based on Support Vector Machine

2021 ◽  
Vol 25 (1) ◽  
pp. 35-39
Author(s):  
Łukasz Glodek ◽  
Szymon Bysko ◽  
Witold Nocoń
Keyword(s):  
Support Vector Machine ◽  
Quality Assessment ◽  
Industry 4.0 ◽  
Assessment Method ◽  
Support Vector ◽  
Model Quality ◽  
Digital Twin ◽  
Model Quality Assessment ◽  
Virtual Commissioning ◽  
Quality Assessment Method

This paper proposes a model quality assessment method based on Support Vector Machine, which can be used to develop a digital twin. This work is strongly connected with Industry 4.0, in which the main idea is to integrate machines, devices, systems, and IT. One of the goals of Industry 4.0 is to introduce flexible assortment changes. Virtual commissioning can be used to create a simulation model of a plant or conduct training for maintenance engineers. On a branch of virtual commissioning is a digital twin. The digital twin is a virtual representation of a plant or a device. Thanks to the digital twin, different scenarios can be analyzed to make the testing process less complicated and less time-consuming. The goal of this work is to propose a coefficient that will take into account expert knowledge and methods used for model quality assessment (such as Normalized Root Mean Square Error – NRMSE, Maximum Error – ME). NRMSE and ME methods are commonly used for this purpose, but they have not been used simultaneously so far. Each of them takes into consideration another aspect of a model. The coefficient allows deciding whether the model can be used for digital twin appliances. Such an attitude introduces the ability to test models automatically or in a semi-automatic way.

scholarly journals Pork Quality Assessment Through Image Segmentation and Support Vector Machine Implementation

2018 ◽  
Vol 5 (2) ◽  
pp. 15-21
Author(s):  
Raver John S. De Guzman ◽  
◽  
David Nathaniel N. Niro ◽  
Aldous Cesar F. Bueno ◽  
◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Image Segmentation ◽  
Quality Assessment ◽  
Support Vector ◽  
Pork Quality

Water Quality Evaluation Based on Multiclass Fuzzy Support Vector Machine

2010 ◽  
Vol 113-116 ◽  
pp. 708-711 ◽  
Author(s):  
Wei Guo Zhao ◽  
Li Ying Wang
Keyword(s):  
Water Quality ◽  
Support Vector Machine ◽  
Quality Assessment ◽  
Water Quality Assessment ◽  
Training Model ◽  
Assessment Method ◽  
Complex Problem ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Quality Assessment Method

It has been a more complex problem for water quality assessment. And its aim is to well and truly evaluate its degree of pollution for bodies of water, which will be easy to provide some principled projects and criterions for water resource’s protection and their integration application. So, a water quality assessment method based on Multiclass Fuzzy Support Vector Machine is put forward. and a two-step cross-validation was used to search for the best combination of parameters to obtain an optimal training model. The test results show that the method proposed in this paper has an excellent performance on correct ratio compared to BP. It indicated that the performance of the proposed model is practically feasible in the application of water quality assessment.

scholarly journals Using Hierarchical Likelihood Towards Support Vector Machine: Theory and Its Application

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 194795-194807
Author(s):  
Rezzy Eko Caraka ◽  
Youngjo Lee ◽  
Rung-Ching Chen ◽  
Toni Toharudin
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Hierarchical Likelihood ◽  
Machine Theory

LS-SVM Adaptive Back-Stepping Control for a Class of Uncertain Nonlinear Systems

2012 ◽  
Vol 588-589 ◽  
pp. 1409-1413
Author(s):  
Guo Dong Zhu ◽  
Hui Lin ◽  
Chen Wang
Keyword(s):  
Support Vector Machine ◽  
Nonlinear Systems ◽  
Least Squares ◽  
System Stability ◽  
Stability And Convergence ◽  
Support Vector ◽  
Control Law ◽  
Uncertain Nonlinear Systems ◽  
Machine Theory ◽  
Back Stepping Control

Based on back-stepping control design, adaptive control and least squares support vector machine theory, a new least squares support vector machine adaptive back-stepping control law was designed for strictly block type of feedback nonlinear systems control with uncertainties. Least squares support vector machine theory method to approximate a nonlinear function of uncertain nonlinear systems by analyzing the disadvantage of common back-stepping. New control law of the nonlinear systems is achieved without accurate mathematical model. The method overcomes the impact of the bounded uncertainties on the control system. On this basis, the system stability and convergence are proved by Lyapunov method. Simulation results indicate that the designed control law has strong robustness and adaptability, uncertainties that exist in the strict block feedback nonlinear systems.

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