“…Neural Network architectures (NN) have been proposed as a replacement to the CAD formula for microwave devices, (K. C. Gupta,1998), where physical attributes are assumed as inputs to the NN which in turn yields the frequency of operation or wide-band input impedance. This approach has been shown to work for microstrip antennas of standard shapes, (Kerim Guney et al,2002) and impedance; 02 Start Synthesis; 03 Call Algorithm Synthesize A to generate an initial shape; 04 Define and reset subsetFlag to FALSE; 05 For Each branch from the set x,y do { 07 For Each rectangle along a branch (starting from the end) do { 09 Build a subset of applicable rules from the set 6 ... 13; 10 If a subset is not NULL set subsetFLAG to TRUE; 11 Choose a rule from the subset and apply it with a probability of P a = 0.8; } } 14 If subsetFLAG == FALSE goto line 16; 15 Goto Line 04 with a probability of P r = 0.7 16 End Synthesis 17 Obtain an estimate for the input impedance; 18 Obtain an estimate of the resonant frequency; 19 Return estimate of resonant frequency and recomputed input impedance (Heriberto Jose Delgado et al,1998). It is therefore of interest to research on whether NNs can improve the accuracy of the shape grammar in analysis.…”