A Neuro-Fuzzy Scheme for Integrated Input Fuzzy Set Selection and Optimal Fuzzy Rule Generation for Classification
This paper proposes a scheme for designing a classier along with fuzzy set selection. It detects discontinuities in the domain of input and prunes the corresponding fuzzy sets using a neuro-fuzzy architecture. This reduces the size of the network and so the number of rules. The network is trained in three phases. In the rst phase, the network learns the important fuzzy sets. In the subsequent phases, the network is pruned to produce optimal rule set by pruning conicting and less significant rules. We use a four-layered feed-forward network and error back propagation learning. The second layer of the network learns a modulator function for each input fuzzy set that identies the unnecessary fuzzy sets. In this paper, we also introduced the notion of utility factors for fuzzy rules. Rules with small utility factors are less signicant or less used rules and can be eliminated. They are learned and detected by the third layer. After training in phase 3 in its reduced structure, the system retains almost the same level of performance. The proposed system has been tested on synthetic data set and found to perform well.
- 5.Chakraborty, D., Pal, N.R.: Making a Multilayer Perceptron Network Say- “Don’t Know” When It Should. In: ICONIP, Orchid Country Club, Singapore (November 18-22, 2002)Google Scholar
- 7.Krishnapuram, R., Lee, J.: Propagation of uncertainty in neural networks. In: Proc. SPIE Conf. Robot, Computer Vision, Bellingham, WA, pp.377—383 (1988)Google Scholar