College of Artificial Intelligence, Guangxi University for Nationalities, Nanning 530006, China.
Xiangsihu College of Gunagxi University for Nationalities, Nanning, Guangxi 532100, China.
Math Biosci Eng. 2023 Jan;20(2):3768-3792. doi: 10.3934/mbe.2023177. Epub 2022 Dec 12.
Although Extreme Learning Machine (ELM) can learn thousands of times faster than traditional slow gradient algorithms for training neural networks, ELM fitting accuracy is limited. This paper develops Functional Extreme Learning Machine (FELM), which is a novel regression and classifier. It takes functional neurons as the basic computing units and uses functional equation-solving theory to guide the modeling process of functional extreme learning machines. The functional neuron function of FELM is not fixed, and its learning process refers to the process of estimating or adjusting the coefficients. It follows the spirit of extreme learning and solves the generalized inverse of the hidden layer neuron output matrix through the principle of minimum error, without iterating to obtain the optimal hidden layer coefficients. To verify the performance of the proposed FELM, it is compared with ELM, OP-ELM, SVM and LSSVM on several synthetic datasets, XOR problem, benchmark regression and classification datasets. The experimental results show that although the proposed FELM has the same learning speed as ELM, its generalization performance and stability are better than ELM.
尽管极限学习机（ELM）在训练神经网络方面的学习速度比传统的慢梯度算法快数千倍，但 ELM 的拟合精度有限。本文提出了功能极限学习机（FELM），它是一种新颖的回归和分类器。它以功能神经元作为基本计算单元，并使用功能方程求解理论来指导功能极限学习机的建模过程。FELM 的功能神经元功能不是固定的，其学习过程是指估计或调整系数的过程。它遵循极端学习的精神，并通过最小误差原则求解隐藏层神经元输出矩阵的广义逆，而无需迭代以获得最优的隐藏层系数。为了验证所提出的 FELM 的性能，将其与 ELM、OP-ELM、SVM 和 LSSVM 在几个合成数据集、XOR 问题、基准回归和分类数据集上进行了比较。实验结果表明，尽管所提出的 FELM 具有与 ELM 相同的学习速度，但它的泛化性能和稳定性优于 ELM。
