In deep learning models, the inputs to the network are processed using activation functions to generate the output corresponding to these inputs. Deep learning models are of particular importance in analyzing big data with numerous parameters and forecasting and are useful for image processing, natural language processing, object recognition, and financial forecasting. Sigmoid and tangent activation functions, which are traditional activation functions, are widely used in deep learning models. However, the sigmoid and tangent activation functions face the vanishing gradient problem. In order to overcome this problem, the ReLU activation function and its derivatives were proposed in the literature. However, there is a negative region problem in these activation functions. In this study, novel RSigELU activation functions, such as single-parameter RSigELU (RSigELUS) and double-parameter (RSigELUD), which are a combination of ReLU, sigmoid, and ELU activation functions, were proposed. The proposed RSigELUS and RSigELUD activation functions can overcome the vanishing gradient and negative region problems and can be effective in the positive, negative, and linear activation regions. Performance evaluation of the proposed RSigELU activation functions was performed on the MNIST, Fashion MNIST, CIFAR-10, and IMDb Movie benchmark datasets. Experimental evaluations showed that the proposed activation functions perform better than other activation functions.

在深度学习模型中，使用激活函数处理网络的输入，以生成与这些输入相对应的输出。深度学习模型在分析具有众多参数和预测的大数据时特别重要，并且对于图像处理，自然语言处理，对象识别和财务预测非常有用。Sigmoid和切线激活函数是传统的激活函数，已在深度学习模型中广泛使用。但是，S形和切线激活函数面临消失的梯度问题。为了克服这个问题，在文献中提出了ReLU激活功能及其衍生物。但是，在这些激活功能中存在负区域问题。在这项研究中，新颖的RSigELU激活功能 提出了单参数RSigELU（RSigELUS）和双参数（RSigELUD），它们是ReLU，Sigmoid和ELU激活函数的组合。提出的RSigELUS和RSigELUD激活函数可以克服消失的梯度和负区域问题，并且可以在正，负和线性激活区域有效。在MNIST，Fashion MNIST，CIFAR-10和IMDb Movie基准数据集上对建议的RSigELU激活功能进行了性能评估。实验评估表明，所提出的激活功能比其他激活功能要好。提出的RSigELUS和RSigELUD激活函数可以克服消失的梯度和负区域问题，并且可以在正，负和线性激活区域有效。在MNIST，Fashion MNIST，CIFAR-10和IMDb Movie基准数据集上对建议的RSigELU激活功能进行了性能评估。实验评估表明，所提出的激活功能比其他激活功能要好。提出的RSigELUS和RSigELUD激活函数可以克服消失的梯度和负区域问题，并且可以在正，负和线性激活区域有效。在MNIST，Fashion MNIST，CIFAR-10和IMDb Movie基准数据集上对建议的RSigELU激活功能进行了性能评估。实验评估表明，所提出的激活功能比其他激活功能要好。