Practical genetic algorithms
Neural networks in business: techniques and applications for the operations researcher
Computers and Operations Research - Neural networks in business
A bibliography of neural network business applications research: 1994–1998
Computers and Operations Research - Neural networks in business
Quality Engineering Using Robust Design
Quality Engineering Using Robust Design
Journal of Global Optimization
Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
Operations Research
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Pattern Recognition and Information Processing Using Neural Networks;Guest Editors: Fuchun Sun,Ying Tan,Cong Wang
Expert Systems with Applications: An International Journal
RF-MEMS switch actuation pulse optimization using Taguchi’s method
Microsystem Technologies
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Combining Neural Networks and Genetic Algorithms to Predict and Reduce Diesel Engine Emissions
IEEE Transactions on Evolutionary Computation
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To combat climate change, many industries have participated in the research on alternative energies. Industrial Technology Research Institute in Taiwan has developed techniques for the solar energy selective absorption film continuous sputtering process. For this extremely complicated process, plenty of parameters would influence the output quality. If parameters settings simply rely on the experience of engineers, the defect rate may increase due to instability. A more reliable approach is desirable to optimize the condition of manufacturing process parameters, thus improving the quality. The present study applies a systematic procedure for the parameter optimization of the absorption film continuous sputtering process. First, possible variables are determined based on collected data and engineering knowledge. Second, Taguchi methods are utilized to search for the significant factors and the optimal level combination of parameters. Finally, the integration of back-propagation neural network, desirability function, and genetic algorithms is used to obtain the optimal parameters setting. According to the experiment results, the performance of the integrated procedure is better than that of Taguchi methods and traditional approach. Furthermore, if applying the integrated method, the saving energy would achieve 9770.53kiloliter of oil equivalent (kLOE) per year, which is 11.2 times the saving kLOE of the traditional paint process.