Stochastic Configuration Based Fuzzy Inference System with Interpretable Fuzzy Rules and Intelligence Search Process

In this paper, a stochastic configuration based fuzzy inference system with interpretable rules (SCFS-IFRs) is proposed to improve the interpretability and performance of determine autonomously an appropriate model structure. The SCFS-IFR first accomplishes through linguistic (ILFRs), which endows clear semantic interpretability. Meanwhile, using incremental learning method on configuration, architecture determined by generation ILFRs under supervision mechanism. addition, particle swarm optimization (PSO) algorithm, intelligence search technique, used in process obtain better random parameters approximation accuracy. SCFS-IFRs verified regression classification benchmark datasets. Regression experiments show that perform best 10 20 data sets, statistically significantly outperforming other eight state-of-the-art algorithms. Classification that, compared six classifiers, achieve higher accuracy interpretation fewer rules.