Department of Electronics Convergence Engineering, Wonkwang University, Iksan 54538, Korea.
School of Mechanical and Intelligent Manufacturing, Jiujiang University, Jiujiang 332005, China.
Sensors (Basel). 2021 Dec 14;21(24):8349. doi: 10.3390/s21248349.
Radial basis function neural networks are a widely used type of artificial neural network. The number and centers of basis functions directly affect the accuracy and speed of radial basis function neural networks. Many studies use supervised learning algorithms to obtain these parameters, but this leads to more parameters that need to be determined, thereby making the system more complex. This study proposes a modified nearest neighbor-based clustering algorithm for training radial basis function neural networks. The calculation of this clustering algorithm is not large, and it can adapt to varying densities. Furthermore, it does not require researchers to set parameters based on experience. Simulation proves that the clustering algorithm can effectively cluster samples and optimize the abnormal samples. The radial basis function neural network based on modified nearest neighbor-based clustering has higher accuracy in curve fitting than the conventional radial basis function neural network. Finally, the path tracking control based on a radial basis function neural network of a magnetic microrobot is investigated, and its effectiveness is verified through simulation. The test accuracy and training accuracy of the radial basis function neural network was improved by 23.5% and 7.5%, respectively.
径向基函数神经网络是一种广泛使用的人工神经网络。基函数的数量和中心直接影响径向基函数神经网络的准确性和速度。许多研究使用监督学习算法来获得这些参数，但这会导致更多需要确定的参数，从而使系统更加复杂。本研究提出了一种基于改进的最近邻聚类算法的径向基函数神经网络训练方法。该聚类算法的计算量不大，可以适应不同的密度。此外，它不需要研究人员根据经验设置参数。仿真证明，该聚类算法能够有效地对样本进行聚类，并对异常样本进行优化。基于改进的最近邻聚类的径向基函数神经网络在曲线拟合方面的准确性高于传统的径向基函数神经网络。最后，研究了基于磁微机器人径向基函数神经网络的路径跟踪控制，并通过仿真验证了其有效性。径向基函数神经网络的测试精度和训练精度分别提高了 23.5%和 7.5%。
