Experimental Evaluation of Intelligent Identification Algorithms Applied to a Wind Tunnel Process

This paper evaluates methodologies of the computational intelligence applied to a nonlinear process identification. The different intelligent methodologies are: hybrid genetic algorithm with simulated annealing and modified Elman neural network. Experimental tests of identification are carried out using a laboratory scale wind tunnel plant. Experiments, for the estimation of the mathematical model of the process, are carried out using step function and pseudo-random binary sequence signals as process input. A white noise signal is used in the validation phase. The performance of each technique, in the estimation and validation phases of the process mathematical model, is presented and discussed.