Activation functions are essential components of neural networks. In this paper, we introduce a new activation function called the Truncated and Signed Square Root (TSSR) function. This function is distinctive because it is odd, nonlinear, monotone and differentiable. Its gradient is continuous and always positive. Thanks to these properties, it has the potential to improve the numerical stability of neural networks. Several experiments confirm that the proposed TSSR has better performance than other stat-of-the-art activation functions. The proposed function has significant implications for the development of neural network models and can be applied to a wide range of applications in fields such as computer vision, natural language processing, and speech recognition.

翻译：激活函数是神经网络的核心组件。本文提出一种新型激活函数——截断有符号平方根（TSSR）函数。该函数具有奇函数、非线性、单调性和可微性等独特性质，其梯度连续且恒为正。得益于这些特性，该函数有望提升神经网络的数值稳定性。多项实验表明，与现有主流激活函数相比，所提出的TSSR函数具有更优性能。该函数对神经网络模型的发展具有重要意义，可广泛应用于计算机视觉、自然语言处理和语音识别等领域。