Using self-organizing artificial neural networks for solving uncertain dynamic nonlinear system identification and function modeling problems

The authors describe novel implementations of the KNN (Kohonen topology-preserving self-organizing neural network) structure as it is applied to nonlinear functions, control system identifications, and switched reluctance motor torque modelings. Specifically, they examine novel training paradigms, including a procedure for initializing and resetting neuron weights, incorporating prior knowledge into a KNN, and preferentially training specific areas of a KNN. Several functions are modeled as examples of the implementation and properties of this technique. Also, the KNN is used to model a nonlinear mapping embedded in a series-parallel control identifier. Finally, a 2-D KNN is used to successfully estimate the torque in a switched reluctance motor