Spatial entropy of one-dimensional cellular neural network

This work investigates the global mosaic pattern and spatial entropy for on e-dimensional cellular neural network (CNN). A novel method is developed to partition the parameter space into finitely many regions. The CNNs, with p arameters in each region, have the same global pattern. An algorithm is als o presented to evaluate the spatial entropy.