Zero Error Nonlinear Correction of Neural Network Optimized by Ant Colony Optimization Algorithm
Min Li · Jisuanji gongcheng · 2013
Aiming at zero error nonlinear correction problem of Current Transformer(CT),this paper presents a nonlinear sensor zero error correction algorithm based on Radial Basis Function(RBF) neural network optimized by Ant Colony Optimization(ACO) method(ACO-RBF).The parameters of RBF neural network are optimized by ACO algorithm,and the optimized RBF neural network is used to correct CT zero error adaptively.Simulation results show that the proposed method can effectively improve the measuring accuracy of automatic test system and reduce measurement error compared with other methods.It can reflect the characteristic of zero.