CMAC神经网络碰撞问题解决方法的研究

Research on Solving the Problem of CMAC Neural Network Collision

针对CMAC神经网络学习算法存在因使用Hash编码技术而产生的实际映射空间地址碰撞问题,提出了一种基于设置权值溢出区解决地址完全碰撞问题的方法,与传统的依靠增加实际映射空间大小解决完全碰撞问题的方法相比,该方法节省了网络的实际权值存储空间,并且在实际地址空间大小相同条件下提高了网络学习的精度.最后,将该方法应用于非线性系统辨识与色彩匹配的样本训练中,实验结果验证了该方法的有效性.

Aiming at the problem of physical mapping address collision in the learning algorithm of CMAC （Cerebellar model articulation controller） neural network caused by Hash mapping in the algorithm, a method based on setting a weight overflow area is proposed in this paper. Compared with traditional learning algorithms which solve the collision problem through increasing the size of real space, this method have the advantages of saving physical memory space and improving the precision of network^learning under the conditions of the same size of real space. Finally, the experiment results show that it works well in the applications of nonlinear system identification and color matching