Logical radial basis function networks: A hybrid model for efficient implementation of classical radial basis function networks

This article introduces a novel approach for fuzzy neural network models that can be used as an approximator. The proposed model is introduced as an adaptive two-level fuzzy inference system. The architecture of the modelis basically a two-layer network of new types of fuzzy-neurons that act as fuzzy IF-THEN rules. The model can be considered a logical version of the radial basis function networks. Genetic algorithms have been adopted as the learning mechanism of the proposed model. Simulations from the area of nonlinear dynamic system identification show the efficiency of the model in terms of error performance and network structure when compared with the classical auto-regressive models and radial basis function networks.