过程神经元网络学习算法及软测量方法的研究

Study on Training Algorithms and Soft Sensing Method of Process Neural Network

研究输入输出以及连接权函数均可为时间函数的过程神经元网络(process neural network,PNN)的学习算法,在基本算法上增加基函数展开系数的规一化处理、权函数动量项调整项,提出学习率自适应调整方法和加速网络收敛速度的改进算法。将过程神经元网络引入到生产过程质量参数的软测量,研究基于正交基展开的过程神经元网络算法,通过分析原网络收敛速度慢等问题,对传统BP算法加以改进,实现了污水处理过程出水水质BOD的预测,仿真取得较好的结果,实践证明这是一种时变过程参数软测量的新方法。

The training algorithms for process neural network （PNN）, which inputs, outputs and network weight function are varied with time, were studied. The normalizing rule based on function orthogonal basis expansion on the original algorithm, and function momentum adjustment item were developed, and learning rate automatic adjustment method and modified algorithms on raising training speed were also introduced. The prediction of quality variables for produce process was realized by using process neural network, through analyzing the reasons of the network low-speed convergence, researching algorithm based on function orthogonal basis expansion for process neural network, referring to the improvement of traditional BP algorithm. By emulation, a good result was gotten to predict effluent BOD from wastewater treatment process, and fact shows that this is a new method for soft sensing of parameters varied with time.