基于RBF神经网络的电站锅炉燃烧系统非线性建模

应用RBF神经网络辨识方法建立了锅炉燃烧系统非线性模型,它可在运行中自动学习,适应很大工况范围及锅炉特性的时变性.应用结果表明所建立的模型能有效跟踪锅炉运行特性,具有很好的泛化能力,为锅炉燃烧系统优化控制和在线预测奠定了基础.

Nonlinear modeling of the boiler combustion system in a power plant based on RBF neural networks

This paper proposes a nonlinear model of a boiler combustion system by identification method based on RBF neural networks. This nonlinear model can adapt to the various working circumstances and the time-variation of operation characteristics by adjusting itself automatically. Application results show, the nonlinear model is capable of tracking the process characteristics effectively, and has good generalization ability. It also builds a strong base for optimal control and on-line prediction of the boiler combustion system.