2017
DOI: 10.3390/jrfm10010006
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Modeling NYSE Composite US 100 Index with a Hybrid SOM and MLP-BP Neural Model

Abstract: Neural networks are well suited to predict future results of time series for various data types. This paper proposes a hybrid neural network model to describe the results of the database of the New York Stock Exchange (NYSE). This hybrid model brings together a self organizing map (SOM) with a multilayer perceptron with back propagation algorithm (MLP-BP). The SOM aims to segment the database into different clusters, where the differences between them are highlighted. The MLP-BP is used to construct a descript… Show more

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Cited by 5 publications
(1 citation statement)
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“…The simple and stable structure of BP neural network results in its easy understanding and fast training. Different from convolutional neural networks, the output of BP neural network can be a series of categories or continuous values, thereby making it suitable to solve classification and regression problems [53,63]. The accuracy of BP neural network has been proven better than traditional regression and classification methods.…”
Section: Set Of Bp Neural Networkmentioning
confidence: 99%
“…The simple and stable structure of BP neural network results in its easy understanding and fast training. Different from convolutional neural networks, the output of BP neural network can be a series of categories or continuous values, thereby making it suitable to solve classification and regression problems [53,63]. The accuracy of BP neural network has been proven better than traditional regression and classification methods.…”
Section: Set Of Bp Neural Networkmentioning
confidence: 99%