Modeling NYSE Composite US 100 Index with a Hybrid SOM and MLP-BP Neural Model

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 descriptive mathematical model that describes the relationship between the indicators and the closing value of each cluster. The model was developed from a database consisting of the NYSE Composite US 100 Index over the period of 2 April 2004 to 31 December 2015. As input variables for neural networks, ten technical financial indicators were used. The model results were fairly accurate, with a mean absolute percentage error varying between 0.16% and 0.38%

Аналоговые ассоциативные интеллектуальные системы

Approach to expand the functionality of associative intelligent systems is considered. Control features an associative interaction of signals in these systems and giving them a second signal system are disclosed. Proposals for the analog implementation of such systems are formulatedРассмотрен подход к расширению функциональных возможностей ассоциативных интеллектуальных систем. Раскрыты особенности управления ассоциативным взаимодействием сигналов в этих системах и наделения их второй сигнальной системой. Сформулированы предложения по аналоговой реализации таких систем

慣性付準ニュートン法に基づくニューラルネットワークの学習アルゴリズムに関する研究

湘南工科大学202