A neural approach of multimodel representation of complex processes
The multimodel approach was recently developed to deal with the issues of complex processes modeling and control. Despite its success in different fields, it still faced with some design problems, and in particular the determination of the models and of the adequate method of validities computation. In this paper, we propose a neural approach to derive different models describing the process in different operating conditions. The implementation of this approach requires two main steps. The first step consists in exciting the system with a rich (e.g. pseudo random) signal and collecting measurements. These measurements are classified by using an adequate Kohonen self-organizing neural network. The second step is a parametric identification of the base-models by using the classification results for order and parameters estimation. The suggested approach is implemented and tested with two processes and compared to the classical modeling approach. The obtained results turn out to be satisfactory and show a good precision. These also allow to draw some interpretations about the adequate validities' calculation method based on classification results.
modeling complex processes multimodel approach kohonen map