Soft sensor based on self-organizing interval type-2 fuzzy neural network and its application

Abstract

To solve the problem of low accuracy of soft sensor of monitoring variables due to strong nonlinear, multivariable coupling, parameters time-varying and large time delays in complex chemical process, a soft sensor modeling method based on self-organizing interval type-2 fuzzy neural network (SOIT2FNN) 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, thereby determining the structure of the interval type-2 fuzzy neural network. Secondly, the relevant parameters of the SOIT2FNN model are corrected by the gradient descent algorithm. Finally, SOIT2FNN is used as a soft sensor model to detect the tail oxygen concentration for uncatalysed oxidation of cyclohexane process. The experimental results show that the soft sensor method based on SOIT2FNN model has the advantages of timely detection and high detection accuracy.