Neural network based system identification of agricultural machinery

摘要

A new method for on-line system identification based on the Self-Organizing Map is presented. The standard Self-Organizing Map (SOM) is extended with Local Linear Mappings. To every node in the SOM along with the input weight two output weights are assigned: one that stores the output part of an input-output pair and one that stores the local gradient matrix (Jacobian) that is calculated from the training pairs. A training algorithm for the Jacobian matrices is derived. The method is tested in system identification of two Agricultural Machines: a flexible Spray Boom and a shaker with a nonlinear spring.