Title :
Modeling Method of Monitoring Nonlinear System by Combining Neural Network with Partial Least Squares
Author_Institution :
Southwest Univ. of Sci. & Technol. Mianyang, Mianyang
Abstract :
By combining the ability of neural network to express nonlinear relationship of system inputs and outputs with the robustness of partial least squares, the complicated nonlinear system input/output model is established in the case of fewer input/output data. This model was used to control and monitor the nonlinear system to achieve its optimum state.
Keywords :
least squares approximations; monitoring; neurocontrollers; nonlinear control systems; input-output model; modeling method; neural network; nonlinear system control; nonlinear system monitoring; partial least squares; principal components; Condition monitoring; Least squares methods; Manufacturing automation; Matrix decomposition; Neural networks; Nonlinear control systems; Nonlinear systems; Personal communication networks; Robustness; System performance; Monitoring; Neural network; Nonlinear system; Partial Least Squares;
Conference_Titel :
Mechatronics and Automation, 2007. ICMA 2007. International Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4244-0828-3
Electronic_ISBN :
978-1-4244-0828-3
DOI :
10.1109/ICMA.2007.4304059
