A manustript of the paper published in the Proceedings of

A technique for dynamic system parameter estimation by using artificialneural-network-like approximators is developed. The technique offers very high speed and high noise immunity. However, in cases where the number of unknown parameters and the estimation accuracy level required are high, the neural network needed to perform the task is rather complex. This causes the training process prohibitevely time-consuming and its results unpredictable. A modular, classifier-approximator neural network architecture is proposed to overcome these deficiences. Results of computer simulation are discussed.