Journal of Intelligence and Information Systems,
Vol. 2, No. 2, December 1996
A Research on Inference Method in Fuzzy Production System
Soo Sup Song
Vol. 2, No. 2, Page: 1 ~ 15
Expert System for Su, pp.rting Operations of the Turbine of Thermal Power Plant
Min-Woo Lee, and Geun-Sik Jo
Vol. 2, No. 2, Page: 17 ~ 27
A Scheduling System for Panel Block Assembly Shop in Shipbuilding using Genetic Algorithms
Hyung Rim Choi, Kwang Ryel Ryu, Kyu Kab Cho, Ho Seob Lim, and Jun Ha Hwang
Vol. 2, No. 2, Page: 29 ~ 42
Keywords : Shipbuliding Scheduling, Genetic Algorithms
S & P 500 Stock Index' Futures Trading with Neural Networks
Jae Hwa Choi
Vol. 2, No. 2, Page: 43 ~ 54
Keywords : neural network, classification/predicition, stock index futures trading
Financial markets are operating 24 hours a day throughout the world and interrelated in increasingly complex ways. Telecommunications and computer networks tie together markets in the from of electronic entities. Financial practitioners are inundated with an ever larger stream of data, produced by the rise of sophisticated database technologies, on the rising number of market instruments. As conventional analytic techniques reach their limit in recognizing data patterns, financial firms and institutions find neural network techniques to solve this complex task. Neural networks have found an important niche in financial a, pp.ications. We a, pp.y neural networks to Standard and Poor's (S&P) 500 stock index futures trading to predict the futures marker behavior. The results through experiments with a commercial neural, network software do su, pp.rt future use of neural networks in S&P 500 stock index futures trading.
Expert System for Deep Drawing Process Planning : DOX
Seong-Jin Cho, Jun-Hwan Oh, Bae-Jung Nam, and Jae-Won Lee
Vol. 2, No. 2, Page: 55 ~ 68
Keywords : expert system, deep drawing, CAPP
Acquisition and Refinement of State Dependent FMS Scheduling Knowledge Using Neural Network and Inductive Learning
Chang-Ouk Kim, Hyeung-Sik Min, and Young-Hae Lee
Vol. 2, No. 2, Page: 69 ~ 83
Keywords : inductive learning, Neural network, real-time scheduling
The objective of this research is to develop a knowledge acquisition and refinement method for a multi-objective and multi-decision FMS scheduling problem. A competitive neural network and an inductive learning algorithm are integrated to extract and refine necessary scheduling knowledge from simulation outputs. The obtained scheduling knowledge can assist the FMS operator in real-time to decide multiple decisions simultaneously, while maximally meeting multiple objective desired by the FMS operator. The acquired scheduling knowledge for an FMS scheduling problem is tested by comparing the desired and the simulated values of the multiple objectives. The result show that the knowledge acquisition and refinement method is effective for the multi-objective and multi-decision FMS scheduling problems.
Korean Terminologies in Expert Systems
Suhn Bum Kwon, and Jae Kyu Lee
Vol. 2, No. 2, Page: 85 ~ 100

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