Quayside Working Condition State Of Neural Network Recognition Technology

The dissertation devotes to research some key technologies based on the system of status monitoring and analysis of Quayside Container Crane (QCC) in WIGAOQIAO Terminal and the raw-material terminal of BAO Steel.A new Pattern Recognition system based on Recurrent Network with bias units is designed and employed to application. During the application, the training swatches needed are obtained by a lot of model tests. The QCC model and the data-acquisition software, through which needed data can be get, are introduced.A model of ANN is developed based on the locations and quantities of sensors on the QCC model. The net can be trained by the gained data.During the Pattern Recognition, the BP-based Network and the bias units-based Recurrent Network are compared, which draw the conclusion that the latter is better than the former. Some analysis about the influence of the optimization of network structure is made.