CMAC Structure Optimization with Q-learning Approach and its Application

Comparing with other neural networks based models, CMAC is successfully applied on many nonlinear control systems because of its computational speed and learning ability. However, for high-dimensional input cases in real application, we often have to make our choice between learning accuracy and memory size. This paper discusses how both the number of layer and step quantization influence the approximation quality of CMAC. By experimental enquiry, it is shown that it is possible to decrease the memory size without losing the approximation quality by selecting the adaptive structural parameters. Based on Qlearning approach, the CMAC structural parameters can be optimized automatically without increasing the complexity of its structure. The choice of this optimized CMAC structure can achieve a tradeoff between the learning accuracy and finite memory size. At last, the application of this Q-learning based CMAC structure optimization approach on the joint angle tracking problem for biped robot is presented.