  The construction of a multilayer perceptron (MLP) as a piecewise low-order
polynomial approximator using a signal processing approach is presented in this
work. The constructed MLP contains one input, one intermediate and one output
layers. Its construction includes the specification of neuron numbers and all
filter weights. Through the construction, a one-to-one correspondence between
the approximation of an MLP and that of a piecewise low-order polynomial is
established. Comparison between piecewise polynomial and MLP approximations is
made. Since the approximation capability of piecewise low-order polynomials is
well understood, our findings shed light on the universal approximation
capability of an MLP.
