Improvement of Judicature De-Noising Algorithm Through Nonlinear Optimization

Aiming to solve the problems of the linear function of the Judicature De-Noising algorithm in the second phase of intelligent artificial respiration bed system,we optimized it by nonlinear function in this paper.Combined with the neural network,we set up a RBF regulations composite index model on the basis of the original algorithm's clinical test.And link to the linear regulations composite index of original algorithm,it can not only improve the accuracy of noise evaluation standards efficiently,but also ensure the feasibility of practical application.Simulating with matrix laboratory software,the results show that the algorithm can accurately distinguish the signal and noise,and response the change of new steady state rapidly.