Abstract
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.
Highlights
We evaluated the clustering results using accuracy (ACC) and adjusted Rand index (ARI); the simulation results show that ACC and ARI using modified nearest neighbor-based clustering (MNNC) are 20% and 10% higher than the common clustering algorithms, respectively
A modified nearest neighbor-based clustering algorithm is proposed in this study that does not necessitate the setting of important parameters relying on past experience, and can perform cluster analysis on samples of different densities and shapes
The number and center of basis functions can be automatically determined by applying this clustering algorithm on the training samples of radial basis function neural network (RBFNN)
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Radial basis function neural networks (RBFNN) have the advantages of fast learning convergence speed and strong approximation ability; they have been used in finite-time trajectory tracking control of n-link robotic manipulators [12], longitudinal speed tracking of autonomous vehicles [13], trajectory tracking for a robotic helicopter [14], and tracking control of a nonholonomic wheel-legged robot in complex environments [15]. In these cases, the control systems based on RBFNN showed good accuracy and stability.
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