Hydrodynamic inline force prediction on vertical cylinders: a comparative study of neural network and its adaptive wavelets (wavenets)

The design of the neural network model and its adaptive wavelets (wavelet networks and wavenets) was used to estimate the wave-induced hydrodynamic inline force acting on a vertical cylinder. The data used to calibrate and validate the models were obtained from an experiment. In the brain, wavelet neural networks (WNNs) use wavelets to activate their hidden layers of neurons. In WNNs, both the position and dilation of the wavelets are optimized along with the weights. In one special approach to this kind of network construction, the position and dilation of the wavelets are fixed and only the weights of the network are optimized. In the present study, the neural network procedure and the above mentioned approach were employed to design a WNN, a so-called wavenet, using feed-forward neural network topology and its training method. Then, a comparison of these two methods was made. Numerical results demonstrate that both networks are capable of predicting hydrodynamic inline force. Furthermore, the combination of the neural network concept and the wavelet theory i.e. wavenet provides a more robust tool rather than standard feed-forward neural network, considering its more appropriate ability to predict any other data which the network had not experienced before. The results of this study can contribute to reducing the errors in future efforts to predict hydrodynamic inline force using WNNs, and thus improve the reliability of that prediction in comparison to the ANN and other methods. Therefore, this method can be applied to relevant engineering projects with satisfactory results.