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System identification from the experimental data plays a vital role for modelbased controller design. Derivation of process model from first principles isoften difficult due to its complexity. The first stage in the development ofany control and monitoring system is the identification and modeling of thesystem. Each model is developed within the context of a specific controlproblem. Thus, the need for a general system identification framework iswarranted. The proposed framework should be able to adapt and emphasizedifferent properties based on the control objective and the nature of thebehavior of the system. Therefore, system identification has been a valuabletool in identifying the model of the system based on the input and output datafor the design of the controller. The present work is concerned with theidentification of transfer function models using statistical modelidentification, process reaction curve method, ARX model, genetic algorithm andmodeling using neural network and fuzzy logic for interacting and noninteracting tank process. The identification technique and modeling used isprone to parameter change & disturbance. The proposed methods are used foridentifying the mathematical model and intelligent model of interacting and noninteracting process from the real time experimental data.

System identification and modeling for interacting and non-interacting tank systems using intelligent techniques