Applying deep learning and wavelet transform for predicting the vibration behavior in variable thickness skew composite plates with intermediate elastic support

In this paper, the vibration behavior features are extracted from combination between Wavelet Transform (WT), and Finite Strip Transition Matrix (FSTM) of skew composite plates (SCPs), with variable thickness, intermediate elastic support. Although, results technique based on previous work done by authors, that show method can reflect plates. Due to method's difficulty in terms of, a lot calculations large number iterations these may not be good choices for quick accurate extracting. Thus, new deep neural network (NN) is designed learn test carrying out extracting important essential information about mode shapes SCP. The give high indications proposed learning promising method, particularly when type structures complicated ambient environment variable.