Institute of Electromechanical and Information Engineering, Putian University, Putian, Fujian, China.
Sci Prog. 2021 Jul-Sep;104(3):368504211026111. doi: 10.1177/00368504211026111.
To improve the network performance of radial basis function (RBF) and back-propagation (BP) networks on complex nonlinear problems, an integrated neural network model with pre-RBF kernels is proposed. The proposed method is based on the framework of a single optimized BP network and an RBF network. By integrating and connecting the RBF kernel mapping layer and BP neural network, the local features of a sample set can be effectively extracted to improve separability; subsequently, the connected BP network can be used to perform learning and classification in the kernel space. Experiments on an artificial dataset and three benchmark datasets show that the proposed model combines the advantages of RBF and BP networks, as well as improves the performances of the two networks. Finally, the effectiveness of the proposed method is verified.
为了提高径向基函数（RBF）和反向传播（BP）网络在复杂非线性问题上的网络性能，提出了一种具有预 RBF 核的集成神经网络模型。该方法基于单个优化 BP 网络和 RBF 网络的框架。通过集成和连接 RBF 核映射层和 BP 神经网络，可以有效地提取样本集的局部特征，从而提高可分性；然后，连接的 BP 网络可以用于在核空间中进行学习和分类。在人工数据集和三个基准数据集上的实验表明，所提出的模型结合了 RBF 和 BP 网络的优点，并提高了两个网络的性能。最后，验证了所提出方法的有效性。
