Polynomial Based Functional Link Artificial Recurrent Neural Network adaptive System for predicting Indian Stocks

A low complexity Polynomial Functional link Artificial Recurrent Neural Network (PFLARNN) has been proposed for the prediction of financial time series data. Although different types of polynomial functions have been used for low complexity neural network architectures earlier for stock market predi...

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Vydáno v:	International journal of computational intelligence systems Ročník 8; číslo 6; s. 1004 - 1016
Hlavní autoři:	Bebarta, D. K., Biswal, Birendra, Dash, P. K.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Dordrecht Springer Netherlands 01.12.2015 Springer Nature B.V Springer
Témata:	Accuracy Adaptive systems backpropagation learning algorithm Basis functions Chaos theory Comparative studies Complexity Computer architecture differential evolution Evolutionary algorithms Evolutionary computation IBM stock indices Machine learning MAPE Mathematical models Model accuracy Neural networks Parameters PFLARNN Polynomial functions Polynomials Recurrent neural networks Research Article Securities markets Time series Weight differential evolution Polynomial functions backpropagation learning algorithm PFLARNN IBM stock indices MAPE AMAPE
ISSN:	1875-6891, 1875-6883, 1875-6883
On-line přístup:	Získat plný text
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