Design for recurrent fuzzy neural networks using MSC-MFS and PSO-MBP

A novel hybrid learning algorithm for designing a TSK-type recurrent fuzzy neural network (RFNN) is proposed in this paper. The whole designing process includes two stages, i.e., structure identification and parameter optimization. The structure identification includes mean shift clustering (MSC) and mean firing strength (MFS). The MSC is used to partition the input space and the mean firing strength (MFS) is employed to prune the redundant rule neurons. After the structure identification is performed, we adopt the PSO to adjust the free parameters of the RFNN and generate the near-optimal free parameters solution. Then, MBP is used to continue the learning process until the terminal condition is satisfied. The proposed hybrid learning algorithm achieves superior performance in learning accuracy.