The main advantages of DE are its capability of solving optimization problems which require minimization process with nonlinear, non-differentiable and multi-modal objective functions.
This paper proposes a two-dimensional Bayesian method for monitoring processes with both nonlinear and multimode characteristics.
The controller can be used to control processes with highly nonlinear or even discontinuous static functions, while keeping simple controller structure and a very low computational burden.
Results are presented for the semiglobal stabilization of a continuous stirred tank reactor, which is a process with high nonlinear behavior.
Rather than modeling the overall process with a nonlinear model, it is proposed to represent the process by a composite model of a linear model (LM) and a feedforward neural network (FNN).
For comparison to a process
Consistent with this, insulin-stimulated glucose flux is a saturable, receptor-mediated process with a nonlinear dose-effect curve [ 25, 26].
There is another method capable of coping with nonlinear processes and observation equations, and non-Gaussian noise models, which is called the Gaussian mixture sigma point particle filter (GMSPPF) [10].
The proposed method for controller design is tested on a nonlinear experimental Four-Tank benchmark process and is compared with nonlinear MVCs designed by neural networks.
Nonlinear state prediction is of crucial importance to design controllers for nonlinear processes with input time delay.
