Granular Neural Networks With Evolutionary Interval Learning

Authors:
Yan-Qing Zhang;B. Jin;Yuchun Tang
Affiliations:
Georgia State Univ., Atlanta;-;-
Venue:
IEEE Transactions on Fuzzy Systems
Year:
2008

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Abstract

To deal with different membership functions of the same linguistic term, a new interval reasoning method using new granular sets is proposed based on Yin Yang methodology. To make interval-valued granular reasoning efficiently and optimize interval membership functions based on training data effectively, a granular neural network (GNN) with a new high-speed evolutionary interval learning is designed. Simulation results in nonlinear function approximation and bioinformatics have shown that the GNN with the evolutionary interval learning is able to extract interval-valued granular rules effectively and efficiently from training data by using the new evolutionary interval learning algorithm.