Liu Tong, Chen Sheng, Li Kang, Gan Shaojun, Harris Chris J
IEEE Trans Cybern. 2023 Dec;53(12):7906-7919. doi: 10.1109/TCYB.2023.3235155. Epub 2023 Nov 29.
Multioutput regression of nonlinear and nonstationary data is largely understudied in both machine learning and control communities. This article develops an adaptive multioutput gradient radial basis function (MGRBF) tracker for online modeling of multioutput nonlinear and nonstationary processes. Specifically, a compact MGRBF network is first constructed with a new two-step training procedure to produce excellent predictive capacity. To improve its tracking ability in fast time-varying scenarios, an adaptive MGRBF (AMGRBF) tracker is proposed, which updates the MGRBF network structure online by replacing the worst performing node with a new node that automatically encodes the newly emerging system state and acts as a perfect local multioutput predictor for the current system state. Extensive experimental results confirm that the proposed AMGRBF tracker significantly outperforms existing state-of-the-art online multioutput regression methods as well as deep-learning-based models, in terms of adaptive modeling accuracy and online computational complexity.
在机器学习和控制领域，非线性和非平稳数据的多输出回归在很大程度上尚未得到充分研究。本文开发了一种自适应多输出梯度径向基函数（MGRBF）跟踪器，用于多输出非线性和非平稳过程的在线建模。具体而言，首先通过一种新的两步训练过程构建一个紧凑的MGRBF网络，以产生出色的预测能力。为了提高其在快速时变场景中的跟踪能力，提出了一种自适应MGRBF（AMGRBF）跟踪器，该跟踪器通过用一个新节点替换性能最差的节点来在线更新MGRBF网络结构，该新节点会自动对新出现的系统状态进行编码，并作为当前系统状态的完美局部多输出预测器。大量实验结果证实，所提出的AMGRBF跟踪器在自适应建模精度和在线计算复杂度方面显著优于现有的在线多输出回归方法以及基于深度学习的模型。
