PROJECTIONAL DYNAMIC NEURAL NETWORK OBSERVER

The projectional method is applied to describe a novel class of dynamic neural network (DNN) observers, which tourn out to be useful when an uncertain nonlinear system, affected by external perturbations keeps its states in an a priori known compact set, defined by given state constraints (usually having physical meaning) independently of the measurement noise effects. The learning law for the weights associated adjustment with the DNN observation problem is derived. The illustrative example dealing with the soil contaminants degradation problem, proves the nice workability of the suggested approach.