A data-driven ADP with RBF network and LSM learning algorithm

Abstract

ADP is an effective optimal method. However, the optimality depends on its network structure and training algorithm. This paper adopts RBF neural network to realize its critic and action networks after a detailed analysis on ADP. The LSM method is introduced as training algorithm, and a novel basis function is defined, which achieves global optimization and online control. The validity is verified by finding the optimal point through local minimums.