Applications of Artificial Neural Networks in Optimum Designs

• Main Authors: Jiann-Horng Lee, 李建宏
• Other Authors: Ting-Yu Chen
• Format: Others
• Language: zh-TW
• Published: 1999
• Online Access: http://ndltd.ncl.edu.tw/handle/22004207865664001058

碩士 === 國立中興大學 === 機械工程學系 === 87 === This thesis deals with the optimum design problems using artificial neural networks. The artificial neural networks are constructed to predict values of the objective and constraint functions. The optimum solutions are found by using mathematical programmings and neural networks. To yield accurate neural networks, the strategy of reducing design space is used to construct more accurate neural networks. The way of reducing design space depends on the number of design variables. The central composite design (CCD) is used to create training patterns in the reduced design domain. The neural networks are then trained and tested using these data. After constructing the neural networks for objective as well as constraint functions, the optimization solver DOT/DOC is utilized to solve the first-stage optimum design problem. After obtaining the initial optimum solution, next neural network models are then constructed in an even smaller design space around the initial optimum point. The final optimum solution is found by using these newly established neural network models. The obtained optimum solutions using neural networks are compared with those obtained by finite element analyses or experimental data.