Combination of radial basis function (RBF) and time delayed neural networks (TDNN) for fault diagnosis of automobile transmission gears using general parameter learning and adaptation

By taking advantages of fuzzy systems and neural networks, a fast and accurate Sugeno's type-I fuzzy system (Type-I fuzzy system) is implemented with the combination of the Gaussian radial basis function network (GP-RBFN) and the time delayed neural network (TDNN), which is based on local modeling using fast general parameter (GP) learning and adaptive algorithms. The proposed GP algorithm applied to adaptation and learning for neural networks is very suitable to parameter optimization of such local linear models in blended multiple model structures. It is applied to a fault detection application. It is experimentally confirmed that the developed fuzzy neural network is more accurate and faster than the RBFN.