A hybrid recurrent neural network for machining process modeling

Abstract

A new hybrid recurrent neural network (HRNN) for machining process modeling is presented based on the diagonal recurrent neural network (DRNN). In order to overcome the weakness of back propagation (BP) algorithm, a generalized entropy square error (GESE) criterion is defined and a dynamic recurrent back propagation algorithm is developed to guarantee the global convergence. The HRNN based on the GESE is then used for nonlinear system identification and neural network modeling of the machining process. The numeral experiments results show that the HRNN has better approximate effectiveness, tracking and dynamic performance than traditional BP neural network. © 2009 Springer Berlin Heidelberg.