Learning of RBF network models for prediction of unmeasured parameters by use of rules extraction algorithm

The paper presents three different methods for learning of normalized RBF network models that are similar in structure to the Takagi-Sugeno fuzzy models. These methods use different groups of parameters for optimization and incorporate a rules extraction algorithm for numerical evaluation of the connection weights, as a part of the optimization. Combinations of the methods give different learning strategies, which are analyzed in the paper through two simulated and one real example.