Fuzzy Recurrent Stochastic Configuration Networks for Industrial Data Analytics

TL;DR

This paper introduces Fuzzy Recurrent Stochastic Configuration Networks (F-RSCNs), a neuro-fuzzy model that enhances interpretability and learning efficiency for industrial data analytics by integrating fuzzy reasoning with stochastic configuration.

Contribution

The paper proposes a novel hybrid neuro-fuzzy model, F-RSCNs, combining multiple sub-reservoirs with fuzzy rules, improving interpretability, learning speed, and robustness in modeling nonlinear industrial systems.

Findings

F-RSCNs outperform classical neuro-fuzzy models in experiments.

The model ensures universal approximation and fast learning.

Online update and convergence analysis enhance model reliability.

Abstract

This paper presents a novel neuro-fuzzy model, termed fuzzy recurrent stochastic configuration networks (F-RSCNs), for industrial data analytics. Unlike the original recurrent stochastic configuration network (RSCN), the proposed F-RSCN is constructed by multiple sub-reservoirs, and each sub-reservoir is associated with a Takagi-Sugeno-Kang (TSK) fuzzy rule. Through this hybrid framework, first, the interpretability of the model is enhanced by incorporating fuzzy reasoning to embed the prior knowledge into the network. Then, the parameters of the neuro-fuzzy model are determined by the recurrent stochastic configuration (RSC) algorithm. This scheme not only ensures the universal approximation property and fast learning speed of the built model but also overcomes uncertain problems, such as unknown dynamic orders, arbitrary structure determination, and the sensitivity of learning parameters in modelling nonlinear dynamics. Finally, an online update of the output weights is performed using the projection algorithm, and the convergence analysis of the learning parameters is given. By integrating TSK fuzzy inference systems into RSCNs, F-RSCNs have strong fuzzy inference capability and can achieve sound performance for both learning and generalization. Comprehensive experiments show that the proposed F-RSCNs outperform other classical neuro-fuzzy and non-fuzzy models, demonstrating great potential for modelling complex industrial systems.