Multilayer perceptrons: From nonlinear regression to inverse problems

The problem of nonlinear least square regression using multi-layered perceptrons is addressed in this paper. A general formulation using maximum likelihood is given and a particular attention is paid to algorithm accounting for uncertainties, The generalization to density estimation is given. In the second part of the paper, a general framework allows to modelize general inverse problems.