NONLINEAR OUTPUT ERROR MODEL USING A SINGLE HIDDEN LAYER NEURAL NETWORK. AN APPLICATION CASE STUDY

摘要

This work is focused on determining a nonlinear output error (OE) model, i.e., a dynamic system, by training a two layer neural network with a Levenberg-Marquardt method. Selected as a case study is application of a dynamic model to predict cutting force in machining processes. A model created by using Artificial Neural Networks (ANN), able to predict the process output, is introduced in order to deal with the characteristics of such an ill-defined process. This model describes the dynamic response of the output before the changes in the process input command (feed rate) and the process parameters (depth of cut). The model provides a sufficiently accurate prediction of cutting force, since the process-dependent specific dynamic properties are adequately reflected.