Hierarchical Fuzzy Neural Network Based on Module Fuzzy Subsystems
Wu Shan Cheng · Kongzhi yu juece · 2006
A hierarchical fuzzy neural network based on module fuzzy subsystems (HM-FNNs) is proposed, which is built based on ellipsoidal basis function and is equivalent to a Takagi-Sugeno-Kang fuzzy system functionally. The HM-FNNs not only remains the full benefits of a traditional FNNs but also suppress the effects of the unwanted phenomenon, “the curse of dimensionality”. It also offers one great advantage that all rule fire strengths are strong on average when passing through subsystem layers. The simulation results show that the proposed method can produce the compact and high performance fuzzy rule-base.