神经网络变体的比较分析:实验研究

S. Vani, T. Madhusudhana Rao, Ch. Kannam Naidu
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引用次数: 3

摘要

神经网络具有从复杂信息中获得意义的非凡能力,可以用来去除那些过于复杂而无法被人类看到的模式。一个准备好的神经网络可以被认为是数据分类的专家。神经网络有人工神经网络(ANN)、前馈神经网络、递归神经网络(RNN)、递归递归神经网络(RRNN)、卷积神经网络(CNN)、模块化神经网络(MNN)、受限玻尔兹曼机(RBM)等。在本文中,我们讨论了ANN、CNN、RNN和RBM的性能,其中CNN以97.81%的准确率胜过其余的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparative Analysis on variants of Neural Networks: An Experimental Study
Neural Networks, with their remarkable capacity to get significance from convoluted information can be utilized to remove patterns that are too composite to be in any way seen by humans. A prepared neural network can be thought of as a specialist in the classification of data which is given to analyze. There are different kinds of Neural Networks like Artificial Neural Network (ANN), Feedforward Neural Network, Recurrent Neural Network(RNN), Recursive Recurrent Neural Network (RRNN), Convolutional Neural Network(CNN), Modular Neural Network (MNN), Restricted Boltzmann Machine (RBM) etc. In this paper, we have discussed the performance of ANN, CNN, RNN, and RBM where CNN has outplayed the remaining with accuracy of 97.81%.
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