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
Multi-model predictive control based on the Takagi-Sugeno fuzzy models: a case study
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
The Volterra and Wiener Theories of Nonlinear Systems
The Volterra and Wiener Theories of Nonlinear Systems
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
Multi-modeling: a different way to design intelligent predictors
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Global and Local Modelling in Radial Basis Functions Networks
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Ensembles of ARTMAP-based neural networks: an experimental study
Applied Intelligence
| Hi-index | 0.01 |
Identification of nonlinear systems is an important task for a large number of areas dealing with real-world applications and requirements. Recently, multiple works proposed ''multi-model'' based approaches to model nonlinear systems. Contrary to the conventional point of view, we propose to deem the multi-modeling as building a modular architecture, inspired from Artificial Neural Networks operation mode, where each neuron (module), represented by one of the local models, realizes some higher level transfer function. This article, deals with generalization of this new multi-modeling concept in the frame of nonlinear system's behavior identification and prediction context. Several multi-model construction strategies and identifiers issued architectures are presented and discussed. Experimental results validating presented multi-model based identifiers have been reported and discussed.