Incremental Learning in a Fuzzy Intelligent System
Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence. Uncertainty and Probabilistic Reasoning. Pages 1376-1383.
Abstract
This paper presents an incremental learning algorithm within the framework of a fuzzy intelligent system. The incremental learning algorithm is based on priority values attached to fuzzy rules. The priority value of a fuzzy rule is generated based on the fuzzy belief values of the fuzzy rule derived from the training data. The fuzzy incremental algorithm has three important properties. It can detect and recover from incorrect knowledge once new knowledge is available; it will not lose the useful knowledge generated from the old data while it attempts to learn from new data; and it provides a mechanism allowing to emphasize on knowledge learnt from the new data. The incremental fuzzy learning algorithm has been implemented in a fuzzy intelligent system for automotive engineering diagnosis. Its performance is presented in the paper.
@inproceedings{ijcai1999p195,
title = {Incremental Learning in a Fuzzy Intelligent System},
author = {Murphey, Yi Lu and Chen, Tie Qi},
booktitle = {Proceedings of the Sixteenth International Joint Conference on
Artificial Intelligence, {IJCAI-99}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Thomas Dean},
pages = {1376--1383},
year = {1999},
note = {Uncertainty and Probabilistic Reasoning},
url = {https://ijcai.org/proceedings/1999/195},
}
TY - CPAPER TI - Incremental Learning in a Fuzzy Intelligent System AU - Murphey, Yi Lu AU - Chen, Tie Qi T2 - Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI-99 PB - International Joint Conferences on Artificial Intelligence PY - 1999 SP - 1376 EP - 1383 UR - https://ijcai.org/proceedings/1999/195 ER -Copied
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