Equally important is the identification of the system's structural features that might impede the risk of systemic collapse or endow a system with the ability to recover from a disturbance.
A major task in artificial intelligence concerns the automatic identification of the system behavior.
The proposed MSD methodology starts after identification of the system states as mentioned in the previous section.
Furthermore, these techniques can be utilized for the identification of the system events, phenomena and load characteristics [10].
This involves the identification of the system's identity characters, that is, the set of values (perceptive, relational, functional) through which a system manifests its character.
The results demonstrate that nonlinear model predictive control has advantages over reinforcement learning if uncertainties can be eliminated through identification of the system parameters.
The methodology for the development of the fuzzy model to predict the water quality index involves the following steps: Step I: identification of the system's variables The first and most important step in modelling is the identification of system's input and output variables.
The proposed methodology is based on a comprehensive scheme relying on adaptive controllers designed by means of the on line identification of the system model under diagnosis.
A model-based control design approach, incorporating identification of the system model based on genetic algorithm is carried out to design the controller.
The three stages are: data acquisition using relay feedback; non-parametric identification of the system step response; and parametric model fitting of the identified step response.
