A Hybrid Knowledge-Based Neural-Fuzzy Network Model with Application to Alloy Property Prediction

Authors:
Min-You Chen;Quandi Wang;Yongming Yang
Affiliations:
Key Lab of High Voltage Eng. and Electric New Tech., Education Ministry, School of Electrical Engineering, Chongqing University, Chongqing 400044, China;Key Lab of High Voltage Eng. and Electric New Tech., Education Ministry, School of Electrical Engineering, Chongqing University, Chongqing 400044, China;Key Lab of High Voltage Eng. and Electric New Tech., Education Ministry, School of Electrical Engineering, Chongqing University, Chongqing 400044, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

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Abstract

This paper presents a hybrid modeling method which incorporates knowledge-based components elicited from human expertise into underlying data-driven neural-fuzzy network models. Two different methods in which both measured data and a priori knowledge are incorporated into the model building process are discussed. Based on the combination of fuzzy logic and neural networks, a simple and effective knowledge-based neural-fuzzy network model has been developed and applied to the impact toughness prediction of alloy steels. Simulation results show that the model performance can be improved by incorporating expert knowledge into existing neural-fuzzy models.