Robust Modeling for Industrial Process Based on Frequency Reconstructed Fuzzy Neural Network
Honggui Han, Zecheng Tang, Xiaolong Wu, Hongyan Yang, Junfei Qiao
Abstract
The model bias caused by input outliers is a dramatic obstacle to the application of models in industrial processes. To cope with this problem, this article proposes a robust modeling method based on frequency reconstructed fuzzy neural network (FRFNN) for industrial process. The robust modeling consists of two parts: One is feature extraction, where a Fourier-based filter is developed with input data denoising. It enables the model to suppress high-frequency input noises and burst outliers. The other one is feature representation that is realized with a FRFNN. The soft margins of membership functions of FRFNN are designed with Fourier estimation of outliers, which have the capability of outlier-tolerant for filtered-residual outliers. Moreover, an adaptive gradient descent algorithm is introduced to update the model parameters. Based on the adaptive learning rate decaying with outliers, this algorithm is insensitive to the bias effect of outliers and also maintains convergence. Finally, the proposed robust modeling method is tested on two real-world industrial datasets with input outliers. The experimental results demonstrate that the proposed robust modeling method can strengthen robustness and achieve superior performance over other previous methods.