Quality assurance in networks-a high order neural net approach

We employ recurrent high order neural networks (RHONNs) to determine the unknown values of media characteristics that lead to user satisfaction without violating network limitations. Based on a priori knowledge-measurements, we assume given a nonlinear function that relates media characteristics with user satisfaction, which we further exploit to construct the control error. Based on Lyapunov stability theory weight update laws are developed to guarantee regulation of the user satisfaction error to zero plus boundedness of all other signals in the closed loop. Simulation studies performed on simple but illustrative examples highlight the approach