This paper presents an intelligent system identification methodology for the identification of a realistic model of an ultrasonic position estimation system that uses the difference in the time of arrivals of waves from a transmitter to various receivers. Even though a linearized formulation for the 3D system exists and is currently being used to estimate the position of the transmitter, its accuracy can still be improved further. A neural network approach is developed to train the system based on training sets obtained from the actual system, and it is proposed to use the final trained system to estimate the 3D position in real time. The weights of the neural network are obtained from an innovative procedure using genetic algorithms. Results for a simplified 1D system are presented as proof of concept. The performance of the identified 1D system using genetic algorithms is shown to be comparable to the one using the analytical model. Further, the identified system using genetic algorithms is also shown to be superior to the one using the traditional back propagation method for finding the weights for the neural networks. This work has significant applications in the identification of complex non-linear systems.

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