Inspection process for dimensioning through images and fuzzy logic

This paper presents a hybrid methodology based on a type 1 fuzzy model in singleton version using a 2k factorial design that optimizes the model of the expert system and serves to perform in-line inspection. The factorial design method provides the required database for the creation of the rule base for the fuzzy model and also generates the database to train the expert system. The proposed method was validated in the process of verifying dimensional parameters by means of images compared with the ANFIS and RBFN models which show greater margins of error in the approximation of the function represented by the system compared with the proposed model. The results obtained show that the model has an excellent performance in the prediction and quality control of the industrial process studied when compared with similar expert system techniques as ANFIS and RBFN

A non-singleton type-2 fuzzy neural network with adaptive secondary membership for high dimensional applications

This paper develops a non-singleton type-2 fuzzy neural network (NT2FNN) with type-2 3-dimensional membership functions (MFs) and adaptive secondary membership. A new approach based on the squareroot cubature quadrature Kalman filter (SR-CQKF) is proposed for the training the level of the secondary membership and the centers of membership functions. The consequent parameters are learned by using rule-ordered extended Kalman filter (EKF). To show the applicability and effectiveness of proposed NT2FNN in high dimensional problems, four real-world datasets with 4, 7, 13 and 32 input variables are considered. Additionally, the performance of NT2FNN with the proposed learning algorithm is compared with other well-known neural networks and learning algorithms. The simulations demonstrate that the developed method results in high performance in contrast to the other methods. (C) 2019 Elsevier B.V. All rights reserved