Multi-Network Neural Model for nonlinear systems modeling

This paper proposes a Multi-Network Neural Model (“MNNM”) to deal with complex systems modeling. Indeed, the training of this architecture was performed using a parallel algorithm consisting on training simultaneously all local neural networks. The obtained results show the effectiveness of the “MNNM” compared to the Single-Network Neural Model (“SNNM”). This work will be supported by two examples: a second order non linear system and a chemical reactor.