Fuzzy inference and rule extraction using a neural network

This paper proposes a neural network for building and optimizing fuzzy models. The network can be regarded both as an adaptive fuzzy inference system with the capability of learning fuzzy rules from data, and as a connectionist architecture provided with linguistic meaning. Fuzzy rules are extracted from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data using a modified competitive learning, and rule parameter tuning phase using gradient descent learning. This allows simultaneous definition of the structure and the parameters of the fuzzy rule base. After learning, the network encodes in its topology the essential design parameters of a fuzzy inference system. A well-known classification benchmark is used to illustrate applicability of the proposed neuro-fuzzy hybrid network.