One Near Space Hypersonic Aircraft Neural Networks Dynamic Surface Backstepping Control Design

By using multilayer neural networks and dynamic surface backstepping,one new robust adaptive control design method is proposed for one hypersonic aircraft (HSA) uncertain MIMO nonaffine block control system. We adopt dynamic surface control strategy to eliminate the explosion of terms by introducing a series of first order filters to obtain the differentiation of the virtual control inputs. multi-layers nerual network adjust function to compensate the influence from the uncertain, and design the robust terms to solve the problem from approach error. The stability analysis and simulations demonstrate the good performance of the controller. Nonlinear six-degree-of-freedom (6-DOF) numerical simulation results for a HSA model are presented to demonstrate the effectiveness of the proposed method.

Water Quality Prediction Based on Grey-Support Vector Regression

Water quality prediction has a great significance to evalute the quality development trend of water and make the planning of water processing. In the study, a novel hybrid method of grey model and support vector regression is proposed to improve the prediction accuracy of sypport vector regression. COD is the important composition in the polluting water. Thus, COD is used as evaluation index of polluting water in the paper. The quality prediction values of input and output water of BP nerual network and the hybrid method of grey model and support vector regression are computated respectively. It is indicated that the water quality prediction by using the hybrid method of grey model and support vector regression is superior to BP nerual network.

The Application of Neural Network in Nonlinear System

Neural network theory is widely applied to predictive control system because of its superiority in dealing with nonlinearities therein. Meanwhile, various algorithms for neural network predictive control have been put forward..The paper investigates the application of neural network-based control in nonlinear system. Especially, some current important nerual network-based controls are remarked and the developments are prospected.