The main aim of this paper is to establish a reliable model both for the steady-state and unsteady-state regimes of a nonlinear process. The use of this model should reflect the true behavior of the process under its normal operating conditions and allow distinguishing a normal mode from an abnormal one. In order to obtain this reliable model for the process dynamics, the neural black-box ident...

System identification (SI) is the discipline of inferring mathematical models from unknown dynamic systems using input/output observations such with or without prior knowledge some system parameters. Many valid algorithms are available in literature, including Volterra series expansion, Hammerstein–Wiener models, nonlinear auto-regressive moving average model exogenous inputs (NARMAX) and its d...

This paper presents a new neural netwodc (NN) based Adaptive Backthrough Control (ABC) & e m for both linear and nodinear dynamic plants. A feedforwanl approach p r e s e n t e d here falls into the direct design category. In its simplest form the implementation requires an -on of the process parameters at any sample t . UnWre the other feedforward NN based control schemes the ABC hem comprises...