Zeroing neural network for time-varying convex quadratic programming with linear noise - Jianfeng LI; Zheyu LIU; Yang RONG; Zhan LI; Bolin LIAO; Linxi QU; Zhijie LIU; Kunhuang LIN (Tongxin xuebao, 2023)
Aiming at the problem that linear time-varying noise may have a negative impact on the existing zeroing neural network model to solve TVQP problem, resulting in slow convergence and low accuracy of the model, a double integral enhancement zeroing neural network was proposed.To solve the problem of linear time-varying interference of the noise, the double integral was introduced based on the original ZNN design formula, and a activation function was designed to eliminate the effects of linear time-varying noise.Theoretical analysis proved that the DIEZNN model had convergence and good noise suppression ability.The experimental results show that compared with the traditional gradient neural network and other variable ZNN models, the proposed DIEZNN model has faster convergence and higher accuracy, and can effectively solve the linear time-varying noise.