Type-2 Radial Basis Function Neural Networks Mandami System to Evaluate Quality Features

This paper presents type-1 and type-2 radial basis function networks to evaluate quality features. The proposed methodology fuses the central composite design and the radial basis function neural networks in type-1 or interval type-2 model to generate a network that evaluates quality features in an industrial image processing. The advantages of this proposal include that training is not required to get an accurate result and that the generation of the fuzzy rule base using central composite design method and statistical indicators is simplified. Another advantage is the excellent results obtained with the proposal. Experimentation shows an error reduction of 90% when the interval type 2 Mandami Radial basis function neural network compared against its type-1 counterpart using the Gaussian membership functions onto a radial basis function network.