Critical points for least-squares problems involving certain analytic functions,with applications to sigmoidal nets

This paper deals with nonlinear least-squares problems involving the fitting to data of parameterized analytic functions. For generic regression data, a general result establishes the countability, and under stronger assumptions finiteness, of the set of functions giving rise to critical points of the quadratic loss function. In the special case of what are usually called single-hidden layer neural networks, which are built upon the standard sigmoidal activation tanh(x) (or equivalently (1 +e^–x)^–1), a rough upper bound for this cardinality is provided as well.Supported in part by US Air Force Grant AFOSR-94-0293.