Title :
mL-CNN: a CNN model for reaction-diffusion processes in m-component systems
Author_Institution :
Inst. of Computational Math. & Math. Geophys., Acad. of Sci., Novosibirsk, Russia
Abstract :
A mL-CNN is presented in this paper as a generalization of CNN models of reaction-diffusion processes in nonlinear media with m components. Main properties of the model are considered in accordance with imaginations of the process "mechanisms". Two particular CNN models, an autonomous 2L-CNN and a 2L-CNN with external inputs, are presented as examples of special cases of the mL-CNN. Emergence of some complex phenomena in such particular models are also shown.
Keywords :
cellular neural nets; digital simulation; physics computing; reaction-diffusion systems; autonomous 2L-CNN; cellular neural networks; external inputs; mL-CNN; multicomponent systems; nonlinear media; reaction-diffusion processes; Cellular neural networks; Circuit simulation; Electronic circuits; Geophysics computing; Mathematical model; Mathematics; Neurons; Piecewise linear techniques;
Conference_Titel :
Cellular Neural Networks and Their Applications, 2002. (CNNA 2002). Proceedings of the 2002 7th IEEE International Workshop on
Print_ISBN :
981-238-121-X
DOI :
10.1109/CNNA.2002.1035041