Multivariate Decoupling Control Based on Improved Regularized Extreme Learning Machine

An improved RELM learning algorithm is prospected to solve the long-time training and the poor real-time control in complex nonlinear decoupling control based on neural network inverse method.The RELM with higher learning accuracy and better generalization ability can improve computational efficiency and reduce training time by use of cholesky factorization without square root based on the feature of output weights.And the method is further applied in decoupling control of multivariate discrete system with 3-input and 3-output.Simulation results demonstrate that the proposed method has faster real-time control speed, and has very high practical value.