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Recent Developments of Self-Organising Modeling in Prediction and Analysis of Stock Market (1997)
| Venue: | Microelectron. Reliab |
BibTeX
@ARTICLE{Ivakhnenko97recentdevelopments,
author = {A.G. Ivakhnenko and J.-A. Müller},
title = {Recent Developments of Self-Organising Modeling in Prediction and Analysis of Stock Market},
journal = {Microelectron. Reliab},
year = {1997},
volume = {37},
pages = {1053--1072}
}
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Abstract
: At present, GMDH algorithms give us the only way to get accurate identification and forecasts of different complex processes in the case of noised and short input sampling. In distinction to neural networks, the results are explicit mathematical models, obtained in a relative short time. For ill-defined objects with very big noises better results should be obtained by analogues complexing methods. Neural nets with active neurones should be applied to rise up accuracy of complex objects modelling algorithms. 1. Introduction Problems of complex objects modelling (functions approximation and extrapolation, identification, pattern recognition, forecasting of random processes and events) can be solved in general by deductive logical-mathematical or by inductive sorting-out methods. Deductive methods have advantages in the cases of rather simple modelling problems, when the theory of the object being modelled is known and therefore it is possible to develop a model from physically-b...
Keyphrases
recent development stock market self-organising modeling complex object random process function approximation pattern recognition inductive sorting-out method explicit mathematical model big noise neural net different complex process active neurones introduction problem ill-defined object accurate identification deductive method neural network relative short time short input
