  System identification from the experimental data plays a vital role for model
based controller design. Derivation of process model from first principles is
often difficult due to its complexity. The first stage in the development of
any control and monitoring system is the identification and modeling of the
system. Each model is developed within the context of a specific control
problem. Thus, the need for a general system identification framework is
warranted. The proposed framework should be able to adapt and emphasize
different properties based on the control objective and the nature of the
behavior of the system. Therefore, system identification has been a valuable
tool in identifying the model of the system based on the input and output data
for the design of the controller. The present work is concerned with the
identification of transfer function models using statistical model
identification, process reaction curve method, ARX model, genetic algorithm and
modeling using neural network and fuzzy logic for interacting and non
interacting tank process. The identification technique and modeling used is
prone to parameter change & disturbance. The proposed methods are used for
identifying the mathematical model and intelligent model of interacting and non
interacting process from the real time experimental data.
