Artificial neural system in decision-aiding for large incomplete databases

The authors propose a hybrid knowledge-based model where neural network technology is used in decision-aiding processes to handle large amounts of incomplete information. The proposed model is composed of two major subunits: a decision-making network and a knowledge acquisition module. The decision-...

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Bibliographic Details
Main Authors: Pakzad, S.H., Jin, B., Hurson, A.R.
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Decision making
Decision support systems
Expert systems
Filters
Knowledge acquisition
Neural networks
System testing
Training data
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Summary:The authors propose a hybrid knowledge-based model where neural network technology is used in decision-aiding processes to handle large amounts of incomplete information. The proposed model is composed of two major subunits: a decision-making network and a knowledge acquisition module. The decision-making network, after being trained, is used as a filter. The knowledge acquisition module is responsible for training the decision-making network. It is shown that the neural network, used as a complement to conventional expert systems, has a strong adaptive learning capability in decision-making. However, what constitutes the set of training data can directly affect the quality of the decision to be made. A semi-real incomplete database has been constructed to provide an appropriate test bed for the proposed decision support system. To investigate the feasibility and performance of the proposed system, a number of simulation runs were conducted and these results are presented.< >
DOI:10.1109/ICSMC.1991.169935