基于自组织随机权神经网络的BOD软测量

针对污水处理复杂系统中关键水质参数生化需氧量( biochemical oxygen demand,BOD)难以准确实时预测的问题,在分析污水处理过程相关影响因素的基础上,提出一种基于敏感度分析法的自组织随机权神经网络( self-organizing neural network with random weights,SONNRW)软测量方法。该方法首先通过机理分析选取原始辅助变量,经过数据预处理,之后采用主元分析法对辅助变量进行精选,作为SONNRW的输入变量进行污水处理关键水质参数BOD的预测。 SONNRW算法利用隐含层节点输出及其权值向量计算该隐含层节点对于残差的敏感度,根据敏感度大小对网络隐含层节点进行排序,删除敏感度较低的隐含层节点即冗余点。仿真结果表明：该软测量方法对水质参数BOD的预测精度高、实时性好、模型结构稳定,能够用于污水水质的在线预测。

BOD Soft-sensing Based on SONNRW

Aiming at the problem that biochemical oxygen demand ( BOD) soft-sensing is difficult to be forecasted accurately and in real time, a self-organizing neural network with random weights( SONNRW) is proposed by using the sensitivity analysis method. Firstly, we select the original auxiliary variables using mechanism analysis, then we use PCA method to select variables after the data preprocessing. The selected variables are the input SONNRW to forecast the key water quality parameter BOD. The residual error’ s sensitivities to the hidden nodes are defined by their outputs and weight vectors connecting to the out layer using the sensitivity analysis method. First, we calculate the sensitivities by using the hidden layer outputs and the responding output layer weight vectors. Then the orders are sorted based on the sensitivity of each hidden node. Then those nodes which have lower sensitivities will be pruned by using the Leave-one-out method. By using this method in BOD soft-sensing, experiments show that the pruning NNRW has high prediction accuracy, more streamlined network size and better generalization performance.