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Self-organizing multi-model based identification: application to nonlinear dynamic systems' behavior prediction

Thiaw L., Madani K.

Image Processing & Communications

2005

Vol. 10, no 2

63-74

Recently, multiple works proposed multi-model based approaches to model nonlinear systems. Such approaches could also be seen as some specific approach, inspired from Artificial Neural Network's operation mode, where each neuron, represented by one of the local models, realizes some higher level transfer function. We are involved in nonlinear dynamic systems identification and behavior prediction, which are key steps in several areas of industrial applications. In this paper, two multi-model based identifiers architectures with self-organization capability are presented, in the frame of nonlinear system's, behavior prediction context. Experimental results validating presented multi-model based structures have been reported and discussed.