KOMPARASI FUNGSI AKTIVASI NEURAL NETWORK PADA DATA TIME SERIES

Ibnu Akil
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Abstract

Abstract— The sophistication and success of machine learning in solving problems in various fields of artificial intelligence cannot be separated from the neural networks that form the basis of its algorithms. Meanwhile, the essence of a neural network lies in its activation function. However because so many activation function which are merged lately, it’s needed to search for proper activation function according to the model and it’s dataset used. In this study, the activation functions commonly used in machine learning models will be tested, namely; ReLU, GELU and SELU, for time series data in the form of stock prices. These activation functions are implemented in python and use the TensorFlow library, as well as a model developed based on the Convolutional Neural Network (CNN). From the results of this implementation, the results obtained with the CNN model, that the GELU activation function for time series data has the smallest loss value
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神经网络数据时间序列
摘要:机器学习在解决人工智能各个领域的问题时的复杂性和成功离不开构成其算法基础的神经网络。同时,神经网络的本质在于它的激活函数。但是由于最近合并的激活函数比较多,需要根据模型和使用的数据集寻找合适的激活函数。在本研究中,将测试机器学习模型中常用的激活函数,即;ReLU, GELU和SELU,用于股票价格形式的时间序列数据。这些激活函数是用python实现的,并使用TensorFlow库,以及基于卷积神经网络(CNN)开发的模型。从本次实现的结果来看,使用CNN模型得到的结果是,时间序列数据的GELU激活函数损失值最小
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