RBF Neural Network Based on Particle Swarm Optimization

Abstract

Abstract. This paper develops a RBF neural network based on particle swarm optimization (PSO) algorithm. It is composed of a RBF neural network, whose parameters including clustering centers, variances of Radial Basis Function and weights are optimized by PSO algorithm. Therefore it has not only simplified the structure of RBF neural network, but also enhanced training speed and mapping accurate. The performance and effectiveness of the proposed method are evaluated by using function simulation and compared with RBF neural network. The result shows that the optimized RBF neural network has significant advantages inspect of fast convergence speed, good generalization ability and not easy to yield minimal local results.

MIMO Lyapunov Theory-Based RBF Neural Classifier for Traffic Sign Recognition

“…RBF network establishes a linear function mapping in the output layer. By multiplying (2) and 3, the weighted hidden values are summed to be the output matrix, Y is as follows:…”

“…Traffic sign recognition is important in autonomous vehicular technology for the sake of identifying a sign functionality through visual information capturing via sensors. The usage of neural networks has become increasingly popular in traffic sign recognition recently to classify various kinds of traffic signs into a specific category [1][2][3]. The reason of applying neural networks in traffic sign recognition is that, they can incorporate both statistical and structural information to achieve better performance than a simple minimum distance classifier [4].…”

Retrieving the Lake Trophic Level Index with Landsat-8 Image by Atmospheric Parameter and RBF: A Case Study of Lakes in Wuhan, China

“…It can also overcome problems where the calculation easily falls into the local minimum. The advantages mentioned above indicate that RBF is more suitable than the back propagation (BP) neural network for water quality inversion [13,14].…”