论文标题
RVFL NNS中是否需要直接链接才能进行回归？
Are Direct Links Necessary in RVFL NNs for Regression?
论文作者
论文摘要
随机矢量功能链路网络（RVFL）被广泛用作分类和回归问题的通用近似值。 RVFL的最大优势是快速训练而没有反向传播。这是因为隐藏节点的权重和偏见是随机选择的，并保持未经训练。最近，开发了具有随机学习的替代体系结构，这与RVFL不同，因为它们没有直接的链接和输出层中的偏差项。在这项研究中，我们研究了直接链接和输出节点偏差对RVFL回归性能的影响。为了生成隐藏节点的随机参数，我们使用经典方法和文献中最近提出的两种新方法。我们在具有不同性质的目标函数的几个函数近似问题上测试RVFL性能：非线性，非线性具有强波动，非线性具有线性组件和线性。令人惊讶的是，我们发现直接链接和输出节点偏差在提高典型非线性回归问题的RVFL准确性方面并不发挥重要作用。
A random vector functional link network (RVFL) is widely used as a universal approximator for classification and regression problems. The big advantage of RVFL is fast training without backpropagation. This is because the weights and biases of hidden nodes are selected randomly and stay untrained. Recently, alternative architectures with randomized learning are developed which differ from RVFL in that they have no direct links and a bias term in the output layer. In this study, we investigate the effect of direct links and output node bias on the regression performance of RVFL. For generating random parameters of hidden nodes we use the classical method and two new methods recently proposed in the literature. We test the RVFL performance on several function approximation problems with target functions of different nature: nonlinear, nonlinear with strong fluctuations, nonlinear with linear component and linear. Surprisingly, we found that the direct links and output node bias do not play an important role in improving RVFL accuracy for typical nonlinear regression problems.
