Optimization Design of Structure and Parameters for RBF Neural Network Using Hybrid Hierarchy Genetic Algorithm

Abstract

To improve the optimization design of Radial Basis Function (RBF) neural network, a RBF neural network based on a hybrid Genetic Algorithm (GA) is proposed. First the hierarchical structure and adaptive crossover probability is introduced into the traditional GA algorithm for the improvement, and then the hybrid GA algorithm is used to optimize the structure and parameters of the network. The simulation indicates that the proposed model has a good modeling performance.

RBF Network Based on Improved Niche Hybrid Hierarchy Genetic Algorithm

“…That increases the difficulty in objective convergence and calculation. Hybrid hierarchy genetic algorithm [3] combines the Hierarchy genetic algorithm with the least square method. The chromosome of HHGA only includes the parameters of hidden layer (σ i ; c i ).…”

Application of Elman Neural Network Based on Genetic Algorithm in Initial Alignment of SINS for Guided Projectile

“…Combining the genetic algorithm with the neural network can not only guarantee the real-time performance of the initial alignment but also optimize the operation. The backpropagation (BP) network and the Radial Basis Function (RBF) network optimized using GA have already been widely demonstrated [23,24].…”

Double Closed-Loop Controller Design of Brushless DC Torque Motor Based on RBF Neural Network

“…The RBF neural network is a three-layer feed-forward neural network, which include a single hidden layer. Acted as a local approximation network, RBF can simulate the local adjustment and the acceptance domain of mutual coverage of human brain and has the ability to approach any nonlinear function with any accuracy [2]. This controller has online identification characteristic which are based on dynamic RBF neural network.…”