An Improved Neural Network and Its Applicable Study

摘要：

In this paper, a robust neural network-based on line learning and artificial immune algorithm is proposed for a boiler combustion optimization system. This method involves a model modification and parameter optimization to the normal use of boiler combustion optimization system neural network. Neural network consists of working sets and standby sets of implicit strata real-time adjusted set number. Standby sets changed into working sets when the need of neural network relearned arised. Parameters of neural network are optimized by artificial immune algorithm. Analyzed results and illustrative examples show that the proposed neural network has a fast convergence to the optimal solution and effectively applied to real-time boiler combustion optimization system.

关键词： BP neural network optimize control artificial immune algorithm

作者: Liu Gang Yang Lin

作者单位: Henan University of Technology, Zhengzhou, Henan, 450004, China P.L.A Information Engineering University Zhengzhou, Henan, 450001, China

会议类型: 国际会议

会议名称: 2010 International Conference on Intelligent Computation Technology and Automation(2010 智能计算技术与自动化国际会议 ICICTA 2010)

会议地点: 长沙

会议语种:英文

页码: 567-570

在线出版日期: 2010-05-11（万方平台首次上网日期，不代表论文的发表时间）