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Title page for etd-0830107-160817


URN etd-0830107-160817 Statistics This thesis had been viewed 1419 times. Download 5 times.
Author Mu-min Cheng
Author's Email Address No Public.
Department Mechanical Engineering
Year 2006 Semester 2
Degree Master Type of Document Master's Thesis
Language zh-TW.Big5 Chinese Page Count 105
Title Optimum Design of Perforated Mufflers by Using Neural Network and Genetic Algorithms
Keyword
  • Neural network
  • Optimal design of perforated muffler
  • Genetic algorithm
  • Genetic algorithm
  • Optimal design of perforated muffler
  • Neural network
  • Abstract In this research, the combination of neural network and genetic algorithm (GA) is applied to the optimal design of perforated mufflers. The target frequencies in the optimization process of perforated muffler are 500Hz, 1000Hz, and 2000Hz, respectively. The mathematical model of the muffler is built by means of input data and output data by neural network. Then GA is used to search the dimensions of optimum muffler and sound transmission loss (STL). Finally, substituting the optimal design into the transfer matrix, and deriving STL. The results show that the network model of perforated muffler that is built by neural network simplifies the theoretical analysis, the difference between the exact solutions and STL of optimal muffler is quite small, and can enhance STL efficiently. It is believed that the optimum algorithm proposed in this study can save the cost in developing silencers in industry.
    Advisor Committee
  • Ying-Chung Chang - advisor
  • none - co-chair
  • none - co-chair
  • Files indicate not accessible
    Date of Defense 2007-07-20 Date of Submission 2007-08-30


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