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