Cellular neural networks with nonlinear and delay-type template elements

The cellular neural network (CNN) paradigm is a powerful framework for analog nonlinear processing arrays placed on a regular grid. The authors extend the repertoire of CNN cloning template elements (atoms) by introducing additional nonlinear and delay-type characteristics. With this generalization, several well-known and powerful analog array-computing structures can be interpreted as special cases of the CNN. Moreover, it is shown that the CNN with these generalized cloning templates has a general programmable circuit structure with analog macros and algorithms. The relations with the cellular automaton and the systolic array are analysed. Finally, some robust stability results and the state-space structure of the dynamics are presented.<
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