  Nonlinear observers based on the well-known concept of minimum energy
estimation are discussed. The approach relies on an output injection operator
determined by a Hamilton-Jacobi-Bellman equation and is subsequently
approximated by a neural network. A suitable optimization problem allowing to
learn the network parameters is proposed and numerically investigated for
linear and nonlinear oscillators.
