Reinforcement radial basis function neural networks with an adaptive annealing learning algorithm

This article proposes reinforcement radial basis function neural networks (RBFNNs) to identify dynamical systems. The proposed algorithm adopts a support vector machine (SVM) to determine the initial structure of RBFNNs. After initialization, an adaptive annealing learning algorithm (AALA) is applied to optimize RBFNNs. When utilizing the optimal RBFNNs to identify dynamic systems, researchers often have problems determining the appropriate learning rates for the evolutionary algorithm and generally obtain better values through trial and error. However, these values may not be the best combinations. This paper proposes a systematic architecture method to determine these parameters. First, orthogonal array (OA) matrix experiments are adopted to find an appropriate combination of learning rates. Then the optimal combination for the evolutionary procedure is obtained. In the learning algorithm procedure, an OA-based AALA (OA-AALA) is provided to determine the optimal RBFNNs (OA-AALA-RBFNNs). Reinforcement RBFNNs can be constructed to identify dynamic systems. To demonstrate the superiority of OA-AALA-RBFNNs for system identification, this study compares the simulation results of the proposed OA-AALA-RBFNNs, ARLA-RBFNNs with an annealing robust learning algorithm (ARLA), and OA-ARLA-RBFNNs with an OA-based annealing robust learning algorithm.