On-line prediction of ship roll motion during maneuvering using sequential learning RBF neuralnetworks

The on-line prediction of ship roll motion during maneuvering plays an important role in navigation safety and ship control applications. This paper presents an on-line prediction model of ship roll motion via a variable structure radial basis function neural network (RBFNN), whose structure and parameters are tuned in real time based on a sliding data window observer. The RBFNN is sequentially constructed by adding the new sample in the hidden layer and pruning the obsolete hidden units at each epoch, with the connecting parameters adjusted simultaneously. Gaussian functions with multi-scale kernel width are adopted to provide more flexible representations of model input terms and to achieve better generalization capability. Simulation study of ship roll motion prediction is conducted with measurement data of turning circle test and zigzag test in full-scale sea trial. Results demonstrate that the proposed neural network predictive model can on-line predict the roll angle with high accuracy. The predictive model is also featured with its compact network structure and fast computationalspeed.

► Variable-structure RBF network is implemented for prediction of ship roll motion.
► Network's structure and connecting parameters are real-time adjusted.
► Samples are learned in a sliding data window sequentially.
► Network achieves satisfactory short-term prediction performance.
► Prediction model is featured by high accuracy and low computational burden.