This paper presents a neural network based technique for the
solution of a water system state estimation problem.The technique
combines a neural linear equations solver with a Newton-Raphson
iterations to obtain a solution to an overdetermined set of
nonlinear equations.
The algorithm has been applied to a realistic 34-node water
network. By changing the values of neural network parameters
both the least squares (LS) and least absolute values (LAV)
estimates have been obtained and assessed with respect to their
sensitivity to measurement errors
