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Your search keyword '"Sui, Yongbo"' showing total 26 results
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26 results on '"Sui, Yongbo"'

11. The Faults Diagnostic Analysis for Analog Circuit Based on FA-TM-ELM

Author

Sui Yongbo, Junnian Wang, and Wenxin Yu

Subjects
0209 industrial biotechnology, Engineering, Artificial neural network, Generalization, business.industry, Reliability (computer networking), 02 engineering and technology, Energy consumption, Machine learning, computer.software_genre, Fault detection and isolation, Computer Science::Hardware Architecture, 020901 industrial engineering & automation, Statistical learning theory, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Firefly algorithm, Artificial intelligence, Electrical and Electronic Engineering, business, computer, Extreme learning machine
Abstract

Developing fault detection is very important for improving the equipment reliability and saving energy consumption. As compared with neural networks, extreme learning machine (ELM) is based on statistical learning theory, which has advantages of better classification ability and generalization performance. This paper presents a novel approach for fault detection and diagnosis based on Firefly-Chaos Algorithm and Extreme Learning Machine. The experiment result indicates this proposed method is effective for Analog Circuit fault detection and diagnosis and the model generalization ability is favorable.

Published
2016

26. A New Method of Speed Control for Induction Motor Based On Improved Particle Swarm Optimization

Author

Yu WenXin, LingZhi Yi, and Sui YongBo

Subjects
Electronic speed control, Engineering, business.industry, media_common.quotation_subject, ComputingMethodologies_MISCELLANEOUS, MathematicsofComputing_NUMERICALANALYSIS, Particle swarm optimization, Control engineering, Inertia, Intelligent algorithms, Improved performance, Nonlinear system, Control theory, Multi-swarm optimization, business, Induction motor, media_common
Abstract

Optimization techniques are becoming more popular for the improvement in control of induction motor. Many intelligent algorithms have been used to improve performance of induction motor so for including particle swarm optimization. However, the improved performance may be limited on account of inertia coefficient in particle swarm optimization, which lead to the unbalance between the searching step and searching precision. In this paper, a variable-step nonlinear dynamic inertia weight of particle swarm optimization speed controller is proposed to improve the performance of an induction motor. The experiment results show that the proposed method has excellent performance.

Published
2016

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