한국경영과학회지 (Journal of the Korean Operations Research and Management Science Society)

  • 제23권1호
  • /
  • Pages.109-141
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  • 1998
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  • 1225-1119(pISSN)
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  • 2733-4759(eISSN)
한국경영과학회 (The Korean Operations Research and Management Science Society)
권호 표지

Robustness of Data Mining Tools under Varting Levels of Noise:Case Study in Predicting a Chaotic Process

  • Kim, Steven H. (Graduate School of Management, Korea Advanced Institute of Science and Technology) ;
  • Lee, Churl-Min (Graduate School of Management, Korea Advanced Institute of Science and Technology) ;
  • Oh, Heung-Sik (Graduate School of Management, Korea Advanced Institute of Science and Technology)
  • 발행 : 1998.03.01
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초록

Many processes in the industrial realm exhibit sstochastic and nonlinear behavior. Consequently, an intelligent system must be able to nonlinear production processes as well as probabilistic phenomena. In order for a knowledge based system to control a manufacturing processes as well as probabilistic phenomena. In order for a knowledge based system to control manufacturing process, an important capability is that of prediction : forecasting the future trajectory of a process as well as the consequences of the control action. This paper examines the robustness of data mining tools under varying levels of noise while predicting nonlinear processes, includinb chaotic behavior. The evaluated models include the perceptron neural network using backpropagation (BPN), the recurrent neural network (RNN) and case based reasoning (CBR). The concepts are crystallized through a case study in predicting a chaotic process in the presence of various patterns of noise.

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