Recent Developments of Self-Organising Modeling in Prediction and Analysis of Stock Market
نویسنده
چکیده
Review 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.
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