CMAC Structure Optimization Based on Modified Q-Learning Approach and Its Applications
Comparing with other neural network based models, CMAC has been applied successfully in many nonlinear control systems because of its computational speed and learning ability. However, for high-dimensional input CMAC in real world applications such as robot, the useable memory is finite or pre-allocated, thus 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 modified Q-learning 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, this Q-learning based CMAC structure optimization approach is applied on the walk pattern generating for biped robot and workpiece orientation estimation for robot arm assembly respectively.
KeywordsCMAC neural network Structural parameters Q-learning Structure optimization
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