Highly Influential

- Published 2007

In this paper we present a class of nonlinear neural network models and an associated learning algorithm that always converges to a set of network parameters (e.g., the connection weights) such that the error between the network trajectories and the desired trajectories vanishes, for all initial conditions and system inputs. Our models are the well known… (More)

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