Soft Sensor Modeling for Temperature Measurement of Texaco Gasifier Based on an Improved RBF Neural Network

摘要：

To solve the problem that RBF Neural Networks has a weakness in generality, a new structure of RBF Neural Network called Hybrid RBF Neural Network is studied in this article. Comparing to general RBF networks, the proposed RBF network has an advantage in achieve better classification performance though partition the input domain flexibly and effectively into the hidden-layer. The number of hidden neurons and the network weight values are automatically determined on the basis of fuzzy C-Means algorithm and PSO algorithm under the supervision of the network performance. This learning proposal is applied and testified its advantage in the soft sensor modeling of temperature measurement of Texaco gasifier.

关键词： RBF Neural Network soft sensor modeling FCM Texaco gasifier

作者: Ting Ji Hongbo Shi

作者单位: Research Institute of Automation East China University of Science and Technology Shanghai, China

会议类型: 国际会议

会议名称: 2006 IEEE International Conference on Information Acquisition

会议地点: 山东威海

会议语种:英文

页码: 1147-1151

在线出版日期: 2006-08-20（万方平台首次上网日期，不代表论文的发表时间）