基于TentFWA-GD的RBF神经网络COD在线软测量方法

COD on-line soft measurement based on TentFWA-GD RBF neural network

针对污水处理过程COD难以实时准确测量的问题,提出了基于TentFWA-GD的RBF神经网络软测量方法。为解决现有RBF神经网络用于复杂工业过程软测量建模时存在网络参数难以确定及训练过程易陷入局部极值等问题,进一步提高RBF神经网络模型的预测精度与泛化能力,引入了Tent混沌映射对烟花算法(fireworks algorithm,FWA)进行改进,利用混沌运动的全局遍历性维持FWA的种群多样性并避免算法早熟收敛;将TentFWA算法与GD方法有机融合提出一种改进的RBF神经网络组合训练方法以改善网络的学习能力。将基于TentFWA-GD的RBF神经网络用于构建4个Benchmark函数拟合模型和农村生活污水处理过程COD在线软测量模型。仿真与应用结果表明,相对于其他神经网络模型,该模型具有较低的函数逼近误差和较高的COD预测精度。其中COD软测量模型训练结果的均方误差和平均绝对误差分别为0.18和0.25,测试结果的两种误差分别为0.23和0.36。

With the goal to realize the real-time accurate measurement of chemical oxygen demand(COD)in wastewater treatment process,a soft-measurement method based on TentFWA-GD RBF neural network(NN)was proposed.To solve the problems of network parameters settings and local optima existing in RBF NN based soft sensor modeling for complex industrial processes,as well as improve the model’s prediction precision and generalization ability,tent chaotic mapping was introduced in fireworks algorithm(FWA)to keep the population diversity and avoid the premature convergence by making use of the global ergodicity of chaos movement.Then a novel training method for RBF NN was proposed by combining the improved TentFWA with gradient descent(GD)method to enhance the learning ability.The TentFWA-GD RBF NN was applied to construct the fitting models of four Benchmark functions and the COD soft sensor model of rural domestic sewage treatment process.Simulation and application results showed that the model had lower function approximate error and higher COD prediction precision as compared with other neural network models.In COD soft sensor modeling,the mean square error and mean absolute error of the training results were 0.18 and 0.25,which of the test results were 0.23 and 0.36,respectively.