๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Application of particle swarm optimization and proximal support vector machines for fault detection

โœ Scribed by B. Samanta; C. Nataraj


Publisher
Springer US
Year
2009
Tongue
English
Weight
728 KB
Volume
3
Category
Article
ISSN
1935-3812

No coin nor oath required. For personal study only.


๐Ÿ“œ SIMILAR VOLUMES


Multi-fault classification based on supp
โœ Xianlun Tang; Ling Zhuang; Jun Cai; Changbing Li ๐Ÿ“‚ Article ๐Ÿ“… 2010 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 263 KB

A novel method of training support vector machine (SVM) by using chaos particle swarm optimization (CPSO) is proposed. A multi-fault classification model based on the SVM trained by CPSO is established and applied to the fault diagnosis of rotating machines. The results show that the method of train

Combination of particle-swarm optimizati
โœ Y. Yang; R. S. Chen; Z. B. Ye ๐Ÿ“‚ Article ๐Ÿ“… 2005 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 148 KB

This new circuit (Fig. 8) shows simulated losses of only 15%, which is 30% less than that of Fig. 4. ## CONCLUSION This study has shown that the coupling between two parallel microstrip lines is enhanced by the use of a slot split-ring resonators (SSRRs) defected ground plane. Since the gap betwe

Support vector machine based training of
โœ Wei-Qi Lin; Jian-Hui Jiang; Yan-Ping Zhou; Hai-Long Wu; Guo-Li Shen; Ru-Qin Yu ๐Ÿ“‚ Article ๐Ÿ“… 2006 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 398 KB ๐Ÿ‘ 1 views

## Abstract Multilayer feedforward neural networks (MLFNNs) are important modeling techniques widely used in QSAR studies for their ability to represent nonlinear relationships between descriptors and activity. However, the problems of overfitting and premature convergence to local optima still pos