A Soft Sensing Method Based on Process Neural Network
A new method of soft sensing based on process neural network (PNN) is proposed in this paper. PNN is an extent of traditional neural network, and it is a new configuration of artificial neural network put forward in recent years. The thesis discuss some modified algorithms for raising training speed of PNN, these algorithms are based on function orthogonal basis expansion which exist low-speed convergence in network training. An improved algorithm for BP network based on function orthogonal basis expansion in process neural network for soft sensing is researched. After increasing the normalizing rule on original algorithm, and introducing function momentum adjustment item and learning rate automatically adjustment method for network weight function, which has means of zero and standard deviations of one, the training time of learning algorithm for process neural network is reduced, and a good effect is represented by simulation in wastewater treatment system.
soft sensing process neural network training algorithm wastewater treatment simulation
LIU Zaiwen LIAN Xiaoqin WANG Zhengxiang WANG Xiaoyi HOU Chaozhen
School of Information Engineering Beijing Technology and Business University Beijing, 100037, China; School of Information Engineering Beijing Technology and Business University Beijing, 100037, China School of Information Science and Technology Beijing Institute of Technology Beijing, 100081,China
国际会议
2006 IEEE International Conference on Information Acquisition
山东威海
英文
611-616
2006-08-20（万方平台首次上网日期，不代表论文的发表时间）