本文研究了一类具偏李·普希兹连续和单调增加激活函数的神经网络绝对指数稳定性问题。
This paper investigates the absolute exponential stability of generalized neural networks with a general class of partially Lipschitz continuous and monotone increasing activation functions.
文章给出了单调函数、有界变差函数、绝对连续函数的定义并讨论了三者之间的关系。
The paper gives the definitions of monotonic function, bounded variation function and absolute continuous function, and discusses the relationship of the three.
当单调函数的反函数不能显性表示时，连续型随机变量的分布密度曲线仍可通过参数方程的形式获得。
When a inverse function of monotone function can not show the explicit formula, the distribution density curve of continuous randon variable can be gained with a parametric equation.
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