An interval neural network-based Caputo fractional-order extreme learning machine applied to classification

In this study, interval-valued data is processed using an interval neural network (INN) based on a fractional-order extreme learning machine (FOELM). The traditional gradient-based algorithms of INN learn slowly. Extreme learning machine (ELM) achieves optimal weights for the output layer in a single calculation, which is faster than iterative training algorithms. However, due to the nonlinear constraints of INN, ELM cannot compute the output layer weights through the Moore–Penrose generalized inverse. Therefore, FOELM is proposed to train the INN in this paper, which ensures faster learning and significant performance. Specifically, in a single hidden layer INN, the hidden layer weights are randomly generated and fixed, and the output layer weights are trained by the fractional-order gradient descent method (FGDM). The experimental results indicate that FOELM exhibits faster convergence and superior classification performance compared to alternative algorithms. •A fractional interval extreme learning machine is proposed for fuzzy data processing.•The fractional order of the fit-best network is obtained through fitting simulation.•Modified network outperforms traditional networks with superior simulation results.•Fractional order enhances speed and accuracy in interval extreme learning machines.