scholarly journals Modeling and Compensation of Random Drift of MEMS Gyroscopes Based on Least Squares Support Vector Machine Optimized by Chaotic Particle Swarm Optimization

Sensors ◽  
2017 ◽  
Vol 17 (10) ◽  
pp. 2335 ◽  
Author(s):  
Haifeng Xing ◽  
Bo Hou ◽  
Zhihui Lin ◽  
Meifeng Guo
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Least Squares ◽  
Particle Swarm ◽  
Support Vector ◽  
Random Drift ◽  
Swarm Optimization ◽  
Mems Gyroscopes ◽  
Chaotic Particle Swarm Optimization

scholarly journals Parameter Selection of a Support Vector Machine, Based on a Chaotic Particle Swarm Optimization Algorithm

2015 ◽  
Vol 15 (3) ◽  
pp. 140-149 ◽  
Author(s):  
Huang Dong ◽  
Gao Jian
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Parameter Selection ◽  
Support Vector ◽  
Optimal Parameters ◽  
Swarm Optimization ◽  
Chaotic Particle Swarm Optimization ◽  
Selection Of

Abstract This paper proposes a SVM (Support Vector Machine) parameter selection based on CPSO (Chaotic Particle Swarm Optimization), in order to determine the optimal parameters of the support vector machine quickly and efficiently. SVMs are new methods being developed, based on statistical learning theory. Training a SVM can be formulated as a quadratic programming problem. The parameter selection of SVMs must be done before solving the QP (Quadratic Programming) problem. The PSO (Particle Swarm Optimization) algorithm is applied in the course of SVM parameter selection. Due to the sensitivity and frequency of the initial value of the chaotic motion, the PSO algorithm is also applied to improve the particle swarm optimization, so as to improve the global search ability of the particles. The simulation results show that the improved CPSO can find more easily the global optimum and reduce the number of iterations, which also makes the search for a group of optimal parameters of SVM quicker and more efficient.

Power Transformer Fault Diagnosis Based on Least Squares Support Vector Machine and Particle Swarm Optimization

2011 ◽  
Vol 50-51 ◽  
pp. 624-628
Author(s):  
Xin Ma
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Least Squares ◽  
Power Transformer ◽  
Particle Swarm ◽  
Support Vector ◽  
Risk Minimization ◽  
Swarm Optimization ◽  
Transformer Fault

Dissolved gas analysis (DGA) is an important method to diagnose the fault of power t ransformer. Least squares support vector machine (LS-SVM) has excellent learning, classification ability and generalization ability, which use structural risk minimization instead of traditional empirical risk minimization based on large sample. LS-SVM is widely used in pattern recognition and function fitting. Kernel parameter selection is very important and decides the precision of power transformer fault diagnosis. In order to enhance fault diagnosis precision, a new fault diagnosis method is proposed by combining particle swarm optimization (PSO) and LS-SVM algorithm. It is presented to choose σ parameter of kernel function on dynamic, which enhances precision rate of fault diagnosis and efficiency. The experiments show that the algorithm can efficiently find the suitable kernel parameters which result in good classification purpose.

scholarly journals Cost prediction on fabricated substation considering support vector machine via optimized quantum particle swarm optimization

2020 ◽  
Vol 24 (5 Part A) ◽  
pp. 2773-2780
Author(s):  
Yi Xiong ◽  
Yue Ming ◽  
Xiaohong Liao ◽  
Chuanyu Xiong ◽  
Wu Wen ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Life Cycle ◽  
Least Squares ◽  
Life Cycle Cost ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Support Vector ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization

At present, the prediction of the life cycle cost of fabricated substation is of great significance for the construction of fabricated substation. An enhanced prediction model based on quantum particle swarm optimization (QPSO) via least squares support vector machine is established. The relevant characteristic index of the life cycle of the fabricated substation is used as the input of the model, and the output is the life cycle cost. The simulation results are compared with the prediction results of QPSO optimized least squares support vector machine (LS-SVM), PSO optimized LS-SVM, traditional LS-SVM, and backpropagation neural network, which shows that the QPSO optimized LS-SVM model has better prediction accuracy, can predict and evaluate the life cycle cost more quickly, and can improve the benefits of fabricated substation construction.

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