[freemint]

> The computational cost of "training" a polynomial would be the same as just one iteration of the training algorithm used by typical NNs.

That statement depends heavily on the dimensionality of the problem. Polynomials also have huge problems with discontinuities (even in some higher order derivative) sometimes would require an infinite number of polynomials to smooth out the errors around the discontinuities. (try to fit the Integral of |x| with polynomials)

Fear of NN in control is justified if the networks are poorly understood.

[srean]

Not just that, they tend to blow up when one extrapolates 'too' far from data. This can be controlled for using other basis functions, for example functions in a reproducing kernel Hilbert space, radial basis functions. It is best to choose the basis based upon the data (as RBFs and RKHS bases do) and not chose a basis independent of the data. This applies for polynomials too, choosing a polynomial basis that's orthogonal with respect to the data distribution makes computations much better behaved -- otherwise its common to run into ill conditioned problems that are very sensitive to noise in the data.