Manual-based parameters tuning methods of control system are widely used in many industrial fields. However, better control performances such as faster control speed, more stable, less workload and safer working conditions are expected. Therefore, artificial intelligent based improved classical control (AI-CC) is highly valued in control fields. Main works are as follows: Firstly, wavelet neural network based PID (WNN-PID) method is proposed, BPNN of BPNN based PID method (BPNN-PID) is replaced by WNN. Secondly, multiple linear regression-based wavelet neural network PID (MR-WNN-PID) is proposed, the control parameters are adjusted according to the predicted output of control system. Thirdly, simulations are implemented to compare the performance of the proposed methods and the existing methods. Finally, the stability of the proposed methods is analyzed by theory. Effects are as follows: Firstly, the proposed AI-CC methods have better performances such as faster control speed, smaller overshoot, better ability of antiinterference. Secondly, theoretical analysis of stability of proposed methods is proven. INDEX TERMS Control parameters online tuning; Artificial intelligent control; Auto-tuning control; Wavelet neural network PID, Multiple linear regression-based prediction I.

This paper introduces a novel algorithm to solve the matrix rank minimization problem among all matrices obeying a set of convex constraints. The most popular convex relaxation of the rank minimization problem minimizes the nuclear norm instead of the rank of the matrix. In this paper we are interested in using robust Gaussian function to solve the low-rank matrix completion problem, which is the special case of the rank minimization problem. This regularized problem is a differential smooth convex optimization problem, in which the