Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Decision trees and transient stability of electric power systems
Automatica (Journal of IFAC)
Automatic Learning Techniques in Power Systems
Automatic Learning Techniques in Power Systems
Review: A review of data mining applications for quality improvement in manufacturing industry
Expert Systems with Applications: An International Journal
Dynamic security assessment and load shedding schemes using self organized maps and decision trees
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
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In this paper, the combined use of decision trees and artificial neural networks is examined in the area of quality improvement of industrial processes. The main goal is to achieve a better understanding of different settings of process parameters and to be able to predict more accurately the effect of different parameters on the final product quality. This paper also presents results from the application of the combined decision tree - neural network method to the transformer manufacturing industry. In the environment considered, quality improvement is achieved by increasing the classification success rate of transformer iron losses. The results from the application of the proposed method on a transformer industry demonstrate the feasibility and practicality of this approach for the quality improvement of industrial processes.