Incorporating Fuzzy Knowledge into Neural Networks
A priori Knowledge and Fuzzy Rules
Fuzzy logic is an efficient tool for representing fuzzy rules, such as
heuristics in IF-THEN structures .
In many real-world applications of neural networks, it is not trivial to
collect sufficient training data. On the other hand, there is usually
domain knowledge, heuristics and expert knowledge available. Unfortyunately,
conventional learning algorithms are not able to take advantage of the
knowledge that is not in the form of data pairs. Therefore, it is
very desirable to develop a method that is able to use data as well as
Data and Knowledge for Neural Network Learning
A priori knowledge can be ultilized in neural network learning in the
following to ways:
Knowledge Incorporation through Regularization and Multi-task Learning
Fuzzy knowledge as regularization. By representing knowledge with
fuzzy rules, it is possible to regularize data-driven learning to
speed up convergence and to improve accuracy.
Fuzzy knowledge as a related task. Multi-task learning is very
attractive , however, it is not easy to define a related task. Knowledge
represented with fuzzy logic can be treated as a related task.
A method that is able to incorporate knowledge with the help of
fuzzy rules has been proposed in , which has shown to be effective
in improving learning performance.
 L. Zadeh. Knowledge representation in fuzzy logic. IEEE Transactions on
Knowledge and data Engineering, 1(1):89-100, 1989
 S. Thrun and L. Pratt. Learning to Learn. Kluwer Academic Publishers. 1997
 Y. Jin and B. Sendhoff. Knowledge incorporation into neural networks from fuzzy rules, Neural Processing Letters, 10(3):231-242, 1999
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