Journal of Intelligence and Information Systems,
Vol. 3, No. 1, June 1997
Pricing of Derivative Securities Using Artificial Neural Network
He Youn Cho, and Jin Seol Yang
Vol. 3, No. 1, Page: 1 ~ 12
Expert System for the Design of Pneumatic Systems
Jeung Yeoul Shin, and Jae Won Lee
Vol. 3, No. 1, Page: 13 ~ 30
Second-Order Learning for Complex Forecasting Tasks: Case Study of Video-On-Demand
Steven H. Kim, and Jonghyung Joo
Vol. 3, No. 1, Page: 31 ~ 45
Keywords : Neural networks, case based reasoning, induction
To date, research on data mining has focused primarily on individual techniques to su, pp.rt knowledge discovery. However, the integration of elementary learning techniques offers a promising strategy for challenging a, pp.ications such as forecasting nonlinear processes. This paper explores the utility of an integrated a, pp.oach which utilizes a second-order learning process. The a, pp.oach is compared against individual techniques relating to a neural network, case based reasoning, and induction. In the interest of concreteness, the concepts are presented through a case study involving the prediction of network traffic for video-on-demand.
Predicting Exchange Rates with Modified Elman Network
Beum-Jo Park
Vol. 3, No. 1, Page: 47 ~ 68
Keywords : Predictions of Daily Exchange Rate Returns, Nonlinearity, BDS statistic, Artificial Neural Networks, Backpropagation
This paper discusses a method of modified Elman network(1990) for nonlinear predictions and its a, pp.ication to forecasting daily exchange rate returns. The method consists of two stages that take advantages of both time domain filter and modified feedback networks. The first stage straightforwardly employs the filtering technique to remove extreme noise. In the second stage neural networks are designed to take the feedback from both hidden-layer units and the deviation of outputs from target values during learning. This combined feedback can be exploited to transfer unconsidered information on errors into the network system and, consequently, would improve predictions. The method a, pp.ars to dominate linear ARMA models and standard dynamic neural networks in one-step-ahead forecasting exchange rate returns.
A learning algorithm of fuzzy neural networks with extended fuzzy weights
Young-Su Son, Young-Nam Na, and Sang-Hyun Bae
Vol. 3, No. 1, Page: 69 ~ 81
In this paper, first we propose an architecture of fuzzy neural networks with triangular fuzzy weights. The proposed fuzzy neural network can handle fuzzy input vectors. In both cases, outputs from the fuzzy network are fuzzy vectors. The input-output relation of each unit of the fuzzy neural network is defined by the extention principle of Zadeh. Also we define a cost function for the level sets(i. e., $\alpha$ 수식 이미지-cuts)of fuzzy outputs and fuzzy targets. Then we derive a learning algorithm from the cost function for adjusting three parameters of each triangular fuzzy weight. Finally, we illustrate our a, pp.oach by computer simulation examples.
An Expert System for Air-conditioner Design
Sang-Ho Kim, Se-Hyun Myung, and Soon-Hung Han
Vol. 3, No. 1, Page: 83 ~ 99
Keywords : design expert system, air-conditioner design
A Hot Coil Quality Design Support System using Case Based Reasoning
Young Kwan Ko, Sang Hyuck Park, Min Soo Suh, and Yeo Jong Lim
Vol. 3, No. 1, Page: 101 ~ 109
A Case Based Maintenance Support for Information Systems in COBOL Domain
Wooju Kim, Jae Won Lee, and Jae Kyu Lee
Vol. 3, No. 1, Page: 111 ~ 142
Keywords : CBR(case based reasoning), Software maintence system
A Construction of The Multimedia Expert System For Wargame Support
Hwa-Soo Kim, Moon-Hee Cho, Hong-Kyu Park, and Kyeong-Won Park
Vol. 3, No. 1, Page: 143 ~ 160
Keywords : Corps Battle Simulation, Combat Outcome Based on Rules for Attrition, Close Combat

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