Artificial Neural Network with Genetic Algorithm for Nonlinear Model of Machining Processes
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., weig...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2012
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| Online Access: | http://ndltd.ncl.edu.tw/handle/88047810592371927245 |
| Summary: | 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.
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