Electrospun nanofibers are important in many applications This study evaluated the effect of structural properties of fibers and their mechanical properties. Then the mechanical properties of nanofibres predicted by artificial intelligence. Degree of crystallinity, percentage arrangement, size of crystals, crystal plates away from the X-ray diffraction and crystallinity index from IR spectrum of poly fibers for eight sample have been obtained for poly acrilonitril then stress their strength have been measured. The data, along with 40 percent conventional fiber nano-fibers as training data to the neural network with backward error was used. and the rest 60 percent of nano fibers as test data to the neural network is applied. Measured data presented to a BP neural networks .Finally, the results of predicted and actual test results of nano fibers parallel batch strength, have been compared together. Nod optimized number of hidden layers and training parameters of neural network genetic algorithm was used and the best network with three layers, function training trainbr, 22 Tdadnd the hidden layer, learning rate of 0.04277, Fixed Momentom 0.6483, the parameter Adjusted mu 0.0646, reduction factor mu0.9909 and increased Faktv mu 4.515 was selected. The average percentage error of the network test 8.9046 and the percentage error of Education 0.0068 is. The lowest percentage of error obtained with this training 0.89009 was related to the weights were saved. Then using the weights between input and hidden layer can be concluded that the distance between crystal plates in 1020 for the strength of other parameters has more impact and also the lowest index arrangement had the effect.With weights between output and hidden layers can be concluded that the elongation stress affected more input parameters .
