The identification of Coupled Map Lattice models for autonomous cellular neural network patterns

The identification problem for spatiotemporal patterns which are generated by autonomousCellular Neural Networks (CNN) is investigated in this paper. The application of traditionalidentification algorithms to these special spatiotemporal systems can produce poor models dueto the inherent piecewise nonlinear structure of CNN. To solve this problem, a new type ofCoupled Map Lattice model with output constraints and corresponding identification algorithmsare proposed in the present study. Numerical examples show that the identified CML modelshave good prediction capabilities even over the long term, and the main dynamics of the originalpatterns appears to be well represented.

International Journal of Bifurcation and Chaos, 18(04), 985–996. World Scientific Publishing