Modelling hook times of mobile cranes using artificial neural networks

The hook times of mobile cranes are processes that are of non-linear and discrete nature. Artificial neural network is a data processing technique that lends itself to this kind of problem. Three common artificial neural network architectures - multi-layer feed-forward (MLFF), group method of data handling (GMDH) and general regression neural network (GRNN) - are compared. The results show that the GRNN model aided with genetic algorithm (GA) is most promising in describing the non-linear and discrete nature of the hook times. The MLFF model can also give a moderate level of accuracy in the estimation of hook travelling times of mobile cranes and is ranked second. The GMDH model is outperformed by the former two due to a less promising R-square. © 2004 Taylor & Francis Ltd.