In this paper, the teaching–learning-based optimization-based functional link artificial neural network (FLANN) has been proposed for the real-time identification of Maglev system. This proposed approach has been compared with some of the other state-of-the-art approaches, such as multilayer perceptron–backpropagation, FLANN least mean square, FLANN particle swarm optimization and FLANN black widow optimization. Further, the real-time Maglev system and the identified model are controlled by the Fuzzy PID controller in a closed loop system with proper choice of the controller parameters. The efficacy of the identified model is investigated by comparing the response of both the real-time and identified Fuzzy PID-controlled Maglev system. To validate the dominance of the proposed model, three nonparametric statistical tests, i.e., the sign test, Wilcoxon signed-rank test and Friedman test, are also performed.

本文提出了一种基于教学的基于优化的功能链接人工神经网络（FLANN），用于磁悬浮系统的实时识别。将该提议的方法与其他一些最新方法进行了比较，例如多层感知器反向传播，FLANN最小均方，FLANN粒子群优化和FLANN黑寡妇优化。此外，实时磁悬浮系统和所识别的模型是由模糊PID控制器与控制器参数的适当选择的闭环系统控制。通过比较实时和已识别的模糊PID控制的磁悬浮系统的响应，研究了已识别模型的有效性。为了验证所提出模型的优势，我们进行了三种非参数统计检验，即符号检验，