Application of Least Squares Support Vector Machine in Fault Diagnosis
作者： Yongli Zhang , Yanwei Zhu , Shufei Lin , Xiaohong Liu
DOI: 10.1007/978-3-642-27452-7_26
关键词: Least squares support vector machine 、 Machine learning 、 Computer science 、 Artificial intelligence 、 Equipment failure 、 Least squares 、 Data mining 、 Support vector machine 、 Fault coverage 、 Fault (power engineering)
摘要: In daily life fault diagnosis is widely used production. With the rapid development of science and technology, new high-tech products emerged. It not enough data samples. Conventional approach ineffective. need to find a good method. The least squares support vector machine algorithm proximal applied diagnosis. Through experiments when learning samples enough, equipment failure does reduce classification accuracy has increased even. On training speed been improve cost building reduced. Improve overall system performance
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