Abstract
In recent days, Artificial Neural Network (ANN) can be applied to a vast majority of fields including business, medicine, engineering, etc. The most popular areas where ANN is employed nowadays are pattern and sequence recognition, novelty detection, character recognition, regression analysis, speech recognition, image compression, stock market prediction, Electronic nose, security, loan applications, data processing, robotics, and control. The benefits associated with its broad applications leads to increasing popularity of ANN in the era of 21st Century. ANN confers many benefits such as organic learning, nonlinear data processing, fault tolerance, and self-repairing compared to other conventional approaches. The primary objective of this paper is to analyze the influence of the hidden layers of a neural network over the overall performance of the network. To demonstrate this influence, we applied neural network with different layers on the MNIST dataset. Also, another goal is to observe the variations of accuracies of ANN for different numbers of hidden layers and epochs and to compare and contrast among them.
Abstract (translated)
最近几天，人工神经网络（ANN）可以应用于绝大多数领域，包括商业，医学，工程等。目前采用ANN的最受欢迎的领域是模式和序列识别，新颖性检测，字符识别，回归。分析，语音识别，图像压缩，股市预测，电子鼻，安全，贷款应用，数据处理，机器人和控制。与其广泛应用相关的益处导致ANN在21世纪时代的日益普及。与其他传统方法相比，ANN提供了许多好处，如有机学习，非线性数据处理，容错和自修复。本文的主要目的是分析神经网络隐藏层对网络整体性能的影响。为了证明这种影响，我们在MNIST数据集上应用了具有不同层的神经网络。另外，另一个目标是观察不同数量的隐藏层和时期的ANN精度的变化，并对它们进行比较和对比。
URL
https://arxiv.org/abs/1809.06188
