Dynamic Neural Units for Nonlinear Dynamic Systems Identification
IWANN '96 Proceedings of the International Workshop on Artificial Neural Networks: From Natural to Artificial Neural Computation
Non Linear Process Identification Using a Neural Network Based Multiple Models Generator
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Data driven multiple neural network models generator based on a tree-like scheduler
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
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Recently, multiple works proposed multi-model based approaches to model nonlinear systems. Such approaches could also be seen as some “specific” approach, inspired from ANN 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 nonlinear dynamic behavior prediction, which are key steps in several areas of industrial applications. In this paper, two identifiers architectures issued from the multi-model concept are presented, in the frame of nonlinear system's behavior prediction context. The first one, based on “equation error” identifier, performs a prediction based on system's inputs and outputs. However, if the system's inputs are often accessible, its outputs are not always available in prediction phase. The second one, called “output error” based identifier/predictor needs only the system's inputs to achieve the prediction task. Experimental results validating presented multi-model based structures have been reported and discussed.