Spatial entropy of one-dimensional cellular neural network

Abstract

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