Novel dynamic enhanced robust principal subspace discriminant analysis for high-dimensional process fault diagnosis with industrial applications.

Since the data are often polluted by numerous measured noise or outliers, traditional subspace discriminant analysis is difficult to extract optimal diagnostic information. To alleviate the impact of the problem, a robust principal subspace discriminant analysis algorithm for fault diagnosis is designed. On the premise of decreasing the impact of redundant information, the optimal latent features can be calculated.

Modified canonical variate analysis based on dynamic kernel decomposition for dynamic nonlinear process quality monitoring.

It is crucial to adopt an efficient process monitoring technique that ensures process operation safety and improves product quality. Toward this endeavor, a modified canonical variate analysis based on dynamic kernel decomposition (DKDCVA) approach is proposed for dynamic nonlinear process quality monitoring. Different from traditional canonical variate analysis and its expansive kernel methods, the chief intention of the our proposed method is to establish a partial-correlation nonlinear model between input dynamic kernel latent variables and output variables, and ensures the extracted feature information can be maximized.

Online Learning Algorithm Based on Adaptive Control Theory.

This paper proposes a new online learning algorithm which is based on adaptive control (AC) theory, thus, we call this proposed algorithm as AC algorithm. Comparing to the gradient descent (GD) and exponential gradient (EG) algorithm which have been applied to online prediction problems, we find a new form of AC theory for online prediction problems and investigate two key questions: how to get a new update law which has a tighter upper bound on the error than the square loss? How to compare the upper bound for accumulated losses for the three algorithms? We obtain a new update law which fully utilizes model reference AC theory. Moreover, we present upper bound on the worst-case expected loss for AC algorithm and compare it with previously known bounds for the GD and EG algorithm.