A novel learning framework of CMAC via grey-area-time credit apportionment and grey learning rate

The advantages of CMAC neural network are fast learning convergence, capable of mapping nonlinear functions quickly due to its local generalization of weight updating, simple architecture, easily processing and hardware implementation. In the training phase, the disadvantage of some CMAC models with a larger fixed learning rate is the unstable phenomenon. The smaller learning rate would cause slower convergence speed. In the aspect, we propose grey learning rate for training phase. We incorporate the grey relational analysis with the number of training iterations to get an adequate learning rate for better convergence performance. In addition, a serious problem of learning interference reduces learning speed and accuracy. The idea is that the error correcting must be proportional to the inverse of learning times, trained input area and grey relational grade for the addressed hyper cube. A credit apportionment adopts the idea to provide fast and accurate learning effects. This paper proposes a novel learning framework of CMAC for better performance and real-time applications. From the simulation results, it is evident that the proposed algorithm provides more accurate and fast convergence in the early cycles of training phase and also becomes significant in the follow-up cycles.