一种改进的B样条CMAC网络概念映射方法

针对CMAC网络在输入维数高、量化间距小、样本数量多的情况下导致虚拟存储空间过大的问题,提出一种改进的基于多维存储结构的B样条CMAC网络概念映射方法,无需增加虚拟空间到物理空间的hash映射,避免了地址碰撞问题,与其他映射方法相比,由于该方法只映射量化空间中少量的有规律的地址单元,使得该映射方法实际所需虚拟地址空间远小于其他方法,从而在存储空间受限情况下,可以显著提高网络的学习精度和泛化能力.仿真试验结果表明,在新的映射方法下,B样条CMAC比常规CMAC学习精度高,学习速度快,泛化能力强,对相同结构的B样条CMAC网络,新的映射方法在存储空间大小、学习能力和泛化能力等方面明显优于现有的其他方法.

An improved conceptual mapping method for B-spline CMAC

Aiming at the problem of huge virtual memory caused by high-dimensional input,tiny quantization space and too many samples,an improved conceptual mapping method for B-spline CMAC based on multi-dimensional memory is proposed. This method can avoid the address collision without Hash mapping from virtual memory to physical memory. Compared with the conventional conceptual mapping method,the proposed method needs less virtual memory address space for mapping only a few regular address in quantization space. It has greatly improved the learning precision and generalization capability under the condition of limited physical memory. Simulation results show that B-spline CMAC with the new conceptual mapping method has a higher learning precision,faster learning speed and better generalization capability than conventional CMAC. Meanwhile,the new conceptual mapping method is better than others in the integrated performance of memory,learning and generalization capability for B-spline CMAC with the same structure.