Ontogenic neuro-fuzzy algorithm: F-CID3

The paper introduces ontogenic Fuzzy-CID3 algorithm (F-CID3) which combines a neural network algorithm and fuzzy sets into a single hybrid algorithm which generates its own topology. Two new methods, one based on a concept of a neural fuzzy number tree, and a class separation method are introduced in the paper and utilized in the algorithm. The F-CID3 algorithm is an extension of an ontogenic CID3 algorithm which generates a neural network architecture by minimizing Shannon’s entropy function. The F-CID3 algorithm generates an initial network architecture in the same way as the CID3 algorithm. It subsequently defines grades of membership for fuzzy sets associated with hidden layer nodes where the entropy is first reduced to zero, and then switches entirely to operations on fuzzy sets. This hybrid approach results in a simpler architecture realization than the CID3, with fewer connections. The performance of the algorithm is analyzed on benchmark examples.