A single layer feed-forward neural network are proposed and implemented using the
schematic editor of the Xilinx foundation series 2.1i. First the mathematical model of the
data set (weights and inputs) is presented in a matrix multiplication format. Secondly the
five design stages are presented and implemented without using the finite state machine,
which control the processes of the forward propagation phase, error calculation, and the
training algorithm. Finally the design can be optimized to decrease the total execution time
and to minimize the cost, which eventually will increase the performance and improve the
function density.