Dynamic neural networks for adaptive control of nonlinear systems

In this paper the design of a dynamic neural network (DNN) for modeling and control of a class of minimum-phase controllable nonlinear systems is considered. The dynamic neural network acts as a generic model of the system, which is then used in the derivation of the control signal. The training of the network is based oil a novel scheme that arranges the outputs of the hidden layer of the DNN into a set of orthogonal basis functions. This allows for the derivation of a stable rule for the training of the DNN's weights and does not require random initialization of the weights.