Application of progressed Back-Propagation neural network to multi-cracked prismatic shaft torsion

Based on the Back-Propagation neural network theory, a new method is presented to process the data of the multi-cracked prismatic shaft torsion problem. For a multi-cracked prismatic shaft torsion problem example, an optimized project of Back-propagation training is introduced by using Neural Network toolbox in MATLAB software, and the project is explained in detail and the good learning scheme is given by simulating the experimental results of the torsion rigidity. In the method, the momentum parameter α has intensive influence on the training times, while the learning ratio η has little effect on them. In addition, the training is more effective with the couple hidden layers than that with the single hidden layer. Finally, the stress intensity factor K₃ at the crack tip can be obtained by the project. It is proved that the method is accurate and converged quickly by the example of the experiment.