基于改进PDF技术的间歇过程NFM模型

摘要

Batch process is an typical nolinear production process and can be simulated by a neuro-fuzzy model (NFM). In the previous research, a new model training method called PDF technology was proposed to successfully conquer the weak generalization ability which caused by the MSE rule based model training. But the density function is hard to estimate and the trained model are not stable when the target PDF can not given. To solve these problems, a new window width estimation method is introduced and also a contraction strategy with a PDF predictor is proposed when the target can not be given. Simulation results demonstrate that the proposed methods can get a more accurate density estimation and a more excellent model prediction ability.%间歇过程是一类具有典型复杂非线性特性的生产过程，可以利用模糊神经网络(NFM)建立其输入输出的非线性映射关系。在前期的研究中曾提出过基于概率密度函数（PDF）技术的模型训练方法，成功解决了传统的基于MSE准则训练方法模型泛化能力弱的问题，但又产生了概率密度难以估计及目标PDF未知时模型性能不稳定的问题。针对这两个问题，引入了新的概率密度窗宽估计方法，并提出了在目标PDF未知时采用PDF预估器及其收缩策略的算法。仿真实验表明：该方法能够保证足够的概率密度估计精度和模型预测性能。