Rule Extraction from Fuzzy Neural Networks FuNN: A Method and a Real-World Application
Nikola Kasabov*, Jaesoo Kim*, Robert Kozma* and Tico Cohen**
*Department of Information Science, University of Otago, P.O.Box 56, Dunedin, New Zealand
**Waste Solutions Ltd, P.O.Box 997, Dunedin, New Zealand
The paper presents a method for rule extraction from fuzzy neural networks FuNN. The method is applied to a real problem – adaptive control of a wastewater treatment reactor The method extracts from a trained FuNN fuzzy rules that have coefficients of importance and certainty factors attached to their condition and conclusion parts respectively. The rules can have different levels of abstraction depending on the chosen values of several parameters. General rules can be used for explanation and understanding purposes, while precise and specific rules can be used for inference over new data.