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.

本文介绍了一种名为 Truncated and Signed Square Root (TSSR) 的新型激活函数，具有奇、非线性、单调和可微分的特点，其梯度是连续且始终为正。多个实验证实，所提出的 TSSR 比其他最先进的激活函数具有更好的性能。该函数对于神经网络模型的发展具有重要意义，并可应用于计算机视觉、自然语言处理和语音识别等领域的广泛应用。