Neural networks in designing fuzzy systems for real world applications
Fuzzy Sets and Systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A Methodology Using Neural Network to Cluster Validity Discovered from a Marketing Database
SBRN '08 Proceedings of the 2008 10th Brazilian Symposium on Neural Networks
Rough Granular Computing in Knowledge Discovery and Data Mining
Rough Granular Computing in Knowledge Discovery and Data Mining
Mathematics of FuzzinessBasic Issues
Mathematics of FuzzinessBasic Issues
Rough fuzzy MLP: knowledge encoding and classification
IEEE Transactions on Neural Networks
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There is an increasing tendency in the worldwide automotive market to consume polymeric materials, because of their processability and low cost in high volumes. This need motivates the search for technological improvements to the material performance, even at the product development stage. The purpose of this paper is to predict the cycle time of an injected part according to its molding parameters using a Rough-Neuro Fuzzy Network. The methodology involves the application of Fuzzy Sets to define an inference mechanism that inserts human knowledge about polymer processing into a structured rule basis. The attributes of the molding parameters are described using membership functions and reduced by Fuzzy rules. The Rough Sets Theory identifies the attributes that are important and the Fuzzy relations influence the Artificial Neural Network (ANN) surface response. Thus, the rule basis filtered by Rough Sets is used to train a back-programmed Radial Basis Function (RBF) and/or a Multilayer Perceptron (MLP) Neuro Fuzzy Network. In order to measure the performance of the proposed Rough-Neuro Fuzzy Network, the responses of the unreduced rule basis are compared with the reduced rule basis. The results show that by making use of the Rough-Neuro Fuzzy Network, it is possible to reduce the need for expertise in the construction of the Fuzzy inference mechanism.