  Probabilistic Neural Network (PNN) is a feed-forward artificial neural
network developed for solving classification problems. This paper proposes a
hardware implementation of an approximated PNN (APNN) algorithm in which the
conventional exponential function of the PNN is replaced with gated threshold
logic. The weights of the PNN are approximated using a memristive crossbar
architecture. In particular, the proposed algorithm performs normalization of
the training weights, and quantization into 16 levels which significantly
reduces the complexity of the circuit.
