基于正则化 AdaBound 的区间二型模糊 神经网络软测量建模

针对复杂化工过程中存在强非线性、多变量耦合、参数时变及大时滞等因素,导致监测变量软测量精度不高的问题,提 出了一种基于正则化 AdaBound 的区间二型模糊神经网络(RAIT2FNN) 软测量建模方法。 首先为了解决区间二型神经网络 (IT2FNN)结构难以确定的问题,提出了一种采用激励强度和相似度定义增长和删减指标的自组织产生规则的算法。 该算法利 用激励强度的大小决定是否产生规则,并根据相似度进行规则的删减从而确定了区间二型模糊神经网络的结构。 其次,本文提 出正则化和 AdaBound 相结合的算法对 RAIT2FNN 模型相关参数进行修正,使得不同参数具有有界的自适应学习速率。 最后将 RAIT2FNN 作为软测量模型应用于环己烷无催化氧化过程尾氧浓度预测问题中。 实验结果为测试时间为 0. 008 2,训练 RMSE 为 0. 018 2,测试 RMSE 为 0. 009 6,表明 RAIT2FNN 作为软测量模型具有预测及时且预测精度较高的优点。

A soft sensor modeling method based on the regularized AdaBound interval type-2 fuzzy neural network

The complex chemical process has problems of strong nonlinear, multivariable coupling, parameters time-varying and large time delay, which result in low accuracy of soft sensor. To address these issues, a soft sensor modeling method based on the regularization AdaBound interval type-2 fuzzy neural network (RAIT2FNN) is proposed. Firstly, to solve the problem that the structure of interval type-2 neural network ( IT2FNN) is difficult to determine, an algorithm for self-organizing generation rules that uses firing strength and rule similarity to define growth and deletion indicators is proposed. The algorithm uses the firing strength to determine whether to generate rules, and deletes the rules according to the similarity. In this way, the architecture of the IT2FNN is determined. Secondly, this article proposes AdaBound with regularization to modify the relevant parameters of the RAIT2FNN model. And different parameters have bounded adaptive learning rates. Finally, RAIT2FNN is used as a soft sensor model to predict the tail oxygen concentration for uncatalysed oxidation of cyclohexane process. The experimental results are that the test time is 0. 008 2, the training RMSE is 0. 018 2, and the test RMSE is 0. 009 6, indicating that RAIT2FNN as a soft sensor model has the advantages of timely prediction and high prediction accuracy.