Adaptive inference-based learning and rule generation algorithms in fuzzy neural network for failure prediction

Publication Type:
Conference Proceeding
Citation:
Proceedings of 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2010, 2010, pp. 33 - 38
Issue Date:
2010-12-01
Metrics:
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2010001756OK.pdf301.08 kB
Adobe PDF
Creating an applicable and precise failure prediction system is highly desirable for decision makers and regulators in the finance industry. This study develops a new Failure Prediction (FP) approach which effectively integrates a fuzzy logic-based adaptive inference system with the learning ability of a neural network to generate knowledge in the form of a fuzzy rule base. This FP approach uses a preprocessing phase to deal with the imbalanced data-sets problem and develops a new Fuzzy Neural Network (FNN) including an adaptive inference system in the learning algorithm along with its network structure and rule generation algorithm as a means to reduce prediction error in the FP approach. © 2010 IEEE.
Please use this identifier to cite or link to this item: