Granular Neural Networks With Evolutionary Interval Learning
Yanqing Zhang, Bo Jin, Yuchun Tang · IEEE Transactions on Fuzzy Systems · 2008
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.