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Cellular neural networks with non‐linear and delay‐type template elements and non‐uniform grids
Author(s) -
Roska Tamás,
Chua Leon O.
Publication year - 1992
Publication title -
international journal of circuit theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.364
H-Index - 52
eISSN - 1097-007X
pISSN - 0098-9886
DOI - 10.1002/cta.4490200504
Subject(s) - cellular neural network , computer science , cellular automaton , generalization , grid , template , systolic array , cloning (programming) , type (biology) , topology (electrical circuits) , macro , artificial neural network , linear map , algorithm , theoretical computer science , mathematics , artificial intelligence , very large scale integration , embedded system , pure mathematics , combinatorics , mathematical analysis , ecology , geometry , biology , programming language
The cellular neural network (CNN) paradigm is a powerful framework for analogue non‐linear processing arrays placed on a regular grid. In this paper we extend the current repertoire of CNN cloning template elements (atoms) by introducing additional non‐linear and delay‐type characteristics. In addition, architectures with non‐uniform processors and neighbourhoods (grid sizes) are introduced. With this generalization, several well‐known and powerful analogue array‐computing structures can be interpreted as special cases of the CNN. Moreover, we show that the CNN with these generalized cloning templates has a general programmable circuit structure (a prototype machine) with analogue macros and algorithms. the relations with the cellular automaton (CA) and the systolic array (SA) are analysed. Finally, some robust stability results and the state space structure of the dynamics are presented.