Topology Learning-Based Fuzzy Random Neural Networks for Streaming Data Regression

As a type of evolving-fuzzy system, the evolving-fuzzy-neuro (EFN) system uses the structure inspired by neural networks to determine its parameters (fuzzy sets and fuzzy rules), so EFN system can inherit the advantages of neural networks. However, for streaming data regression, EFN systems still have several drawbacks: determining fuzzy sets is not robust to data sequence; determining fuzzy rules is complex as subspaces that can approximate to a Takagi-Sugeno-Kang (TSK) rule need to be obtained, and many parameters need to be optimized; and it is difficult to detect and adapt to changes in the data distribution, i.e., concept drift, if the output is a continuous variable. Hence, in this article, a novel evolving-fuzzy-neuro system, called the topology learning-based fuzzy random neural network (TLFRNN), is proposed. In TLFRNN, an online topology learning algorithm is designed to self-organize each layer of TLFRNN. Different from current EFN systems, TLFRNN learns multiple fuzzy sets to reduce the impact of noises on each fuzzy set, and a randomness layer is designed, which assigning the probability of each fuzzy set. Also, TLFRNN does not utilize TSK rules; instead uses a simple inference that considers fuzzy and random information of data simultaneously. More importantly, in TLFRNN, concept drift can be detected and adapted easily and rapidly. The experiments demonstrate that TLFRNN achieves superior performance compared to other EFSs.

Automated summary

This article proposes a novel evolving-fuzzy-neuro system called TLFRNN, which self-organizes each layer using online topology learning algorithm and learns multiple fuzzy sets to reduce the impact of noise. By considering both fuzzy and random information in a simple inference, TLFRNN can easily detect and adapt to concept drift.