Although the partial least squares approach is an effective fault detection method, some
issues of nonlinear process monitoring related to key performance indicators (KPIs) still …

For the bilinear system with white noise, the difficulty of identification is that there exists the
product term of the state and input in the system. To overcome this difficulty, we derive the …

For a special class of nonlinear systems (ie, bilinear systems) with autoregressive moving
average noise, this paper gives the input‐output representation of the bilinear systems …

As a multivariate statistical analysis method, canonical correlation analysis (CCA) performs
well for state monitoring of linear processes, but most industrial processes are nonlinear. To …

The relationship between the parameters and the states of state-space systems is nonlinear,
which makes the identification problems of state-space systems complicated. This paper …

Air pollution is a common problem for many countries around the world in the process of
industrialization as well as a challenge to sustainable development. This paper has selected …

It is well-known that mathematical models are the basis for system analysis and controller
design. This paper considers the parameter identification problems of stochastic systems by …

Nonlinearity is extremely common in industrial processes. For handling the nonlinearity
problem, this paper combines artificial neural networks (ANN) with principal component …

The identification for process control systems is considered in this paper based on the
impulse response signals from the discrete measurements. By taking advantage of impulse …

In order to conduct efficient process monitoring of modern industrial system featured with
complexity, distributed and high-dimensional, a distributed dictionary learning is proposed …