Also, the influence on learning convergence from compression mapping in those CMAC models with actual neurons is discussed.
对于存在实际层神经元的CMAC模型，讨论了压缩映射对网络学习收敛性的影响。
CMAC (Cerebellar Model Articulation Controller) is a kind of local learning feed - forward neural network with simple architecture, quick learning convergence and effective implementation.
小脑模型清晰度控制器(CMAC)是一种局部学习前馈网络，结构简单，收敛速度快，易于实现。
This convergence is a huge benefit because it means that the learning curve is easily transferred from one AOP approach to another.
这种聚合有很大的好处，因为这意味着学习曲线很容易从一项AOP技术转移到另外一项AOP技术。
应用推荐