Multilayer perceptrons and radial basis functions are universal robust approximators

The standard risk-sensitive (or exponential quadratic) functional used for robust control and filtering for linear systems is generalized. It is then shown that under relatively mild conditions, a function can be approximated, to any desired degree of accuracy with respect to these general risk-sensitive functionals, by a multilayer perceptron or a radial basis function network.