한국경영과학회:학술대회논문집 (Proceedings of the Korean Operations and Management Science Society Conference)
- 한국경영과학회 1997년도 추계학술대회발표논문집; 홍익대학교, 서울; 1 Nov. 1997
- /
- Pages.181-184
- /
- 1997
Robustness of Learning Systems Subject to Noise:Case study in forecasting chaos
- Kim, Steven H. (Graduate school of management Korea Advanced Institute of Sciecne and Technology) ;
- Lee, Churl-Min (Graduate school of management Korea Advanced Institute of Sciecne and Technology) ;
- Oh, Heung-Sik (Graduate school of management Korea Advanced Institute of Sciecne and Technology)
- 발행 : 1997.10.01
초록
Practical applications of learning systems usually involve complex domains exhibiting nonlinear behavior and dilution by noise. Consequently, an intelligent system must be able to adapt to nonlinear processes as well as probabilistic phenomena. An important class of application for a knowledge based systems in prediction: forecasting the future trajectory of a process as well as the consequences of any decision made by e system. This paper examines the robustness of data mining tools under varying levels of noise while predicting nonlinear processes in the form of 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 Henon process in the presence of various patterns of noise.