Design of robust cellular neural networks

Shows how to systematically design an inputless cellular neural network (CNN), which processes only the information present in the initial state, with prescribed stable and unstable outputs while simultaneously maximizing its robustness with respect to changes of its parameters. This is achieved by combining a generalization of previous results on CNN design with a design centering algorithm based on linear programming. The design process is highly efficient with small numbers of cells, and it can be precisely and flexible controlled. Many kinds of implementation-related constraints may be introduced, including bounded parameters and arbitrary topological restrictions. A nonrigorous but effective practical guideline for shaping the basins of attraction of stable outputs is recommended. A simple example is given and thoroughly discussed.<
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