| 摘要: |
| 基于传统的CMAC神经网络和局部加权回归技术,提出了与传统CMAC(cerebellar model articulation computer)有着同样存储空间量的改进的新CMAC网络New-CMAC,它具有传统的输出和具有其微分信息的输出,因而更适合于自动控制.接着,又提出了其新的学习算法,并研究了其学习收敛性. |
| 关键词: CMAC(cerebellar model articulation computer) 学习收敛 模糊泛集合 学习规则 |
| DOI： |
| 分类号: |
| 基金项目:Supported by the National Natural Science Foundation of China under G rant No.6983004 (国家自然科学基金); British Royal Society |
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| Research on New-CMAC with Differentiability Output and Its Learning Convergence |
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WANG Shi tong,J.F. Baldwin,T.P. Martin
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| Abstract: |
| In this paper, based on conventional CMAC (cerebellar model architecture controller) neural network and locally weighted regression, the improved New CMAC with the same amount of memory as that of conventional CMAC is presented, which has the conventional output and its derivative information output and hence is especially appropriate for automatic control. Accordingly, the new learning algorithm is investigated, and its learning convergence is proved. |
| Key words: CMAC （cerebellar model architecture controller） learning algorithm differentiability output locally weighted regression |
