Artificial Neural Network with Genetic Algorithm for Nonlinear Model of Machining Processes

• Main Authors: Ming-chang Lee, 李明璋
• Other Authors: Jyh-Horng Chou
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/88047810592371927245

碩士 === 國立高雄第一科技大學 === 機械與自動化工程研究所 === 100 === This study an artificial neural network (ANN) model with hybrid Taguchi-genetic algorithm (HTGA) is applied in a nonlinear multiple-input multiple-output (MIMO) model of machining processes. The HTGA in the MIMO ANN model optimizes parameters (i.e., weights of links and biases governing ) input-output relationships in the ANN by directly minimizing root-mean-squared error (RMSE), which is a key performance criterion. Experimental results show that, for nonlinear modeling of machining processes, the proposed MIMO HTGA-based ANN model has better prediction accuracy compared to conventional MIMO-based ANN models with backpropagation that are included in the Matlab toolbox.