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Trajectory assignment by output zeroing

In this paper we investigate the problem of how to impose a state-space trajectory on an artificial neural network of Hopfield type. By defining certain output functions of network states we are able to achieve the required trajectories without requiring training. Our theory is based on nonlinear geometrical methods and we use reliable and accurate numerical algorithms for all our computations.