Multiobjective Evolution of the Deep Fuzzy Rough Neural Network

Jianwei Zhao, Dingjun Chang, Bin Cao, Xin Liu, Zhihan Lyu

Abstract: Deep learning has made remarkable achievements in many fields. However, although fuzzy neural networks with natural interpretability are widely used in prediction and control scenarios, there are very few studies on the deepening of fuzzy systems. By integrating rough set theory, fuzzy rough neural network has unique advantages benefiting from the complementarity of the fuzzy set theory and the rough set theory as well as the powerful learning ability of neural networks. Similarly, research on deep fuzzy rough neural networks is even rarer. In this article, in order to improve the performance of the fuzzy rough neural network and expand its application range, the deep fuzzy rough neural network model is constructed and optimized by stacking blocks of fuzzy rough neural network to imitate the deepening deep neural network based on multiobjective evolution. Each fuzzy rough neural network block is interpretable, and its stacked architecture also has high interpretability. To automatically generate deep fuzzy rough neural network models with high efficiency, a distributed parallel multiobjective neuroevolution framework is developed, thus blocks can be stacked flexibly and deep architecture can be optimized considering multiple optimization objectives of accuracy, interpretability, and generalization simultaneously. In addition, multiobjective evolution is combined with Wang–Mendel method, pseudoinverse, and backpropagation to effectively learn specific parameters. Finally, based on the time series prediction problems, the superiority of the multiobjective deep fuzzy rough neural network evolutionary framework is verified.