Abstract
With an adaptive learning algorithm for fuzzy rules can be generated according to the needs of the specific problems a different number of fuzzy rules, and according to the fuzzy rules to determine the network structure. Determining the structure of the network, due to not randomly generated, and without trial and error, so the more scientific. Prediction in the stock, with different stocks selected, the resulting network structure are not the same, so that the trained network is more focused, be more accurate forecasts for the stock. Experiments show that the improved fuzzy neural network prediction of stock trends better stability on the future operation of the stock market has a certain significance.
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References
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Wei, X.Y., Huang, S.Z. (2012). Improved Fuzzy Neural Network for Stock Market Prediction and Application. In: Hou, Z. (eds) Measuring Technology and Mechatronics Automation in Electrical Engineering. Lecture Notes in Electrical Engineering, vol 135. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-2185-6_34
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DOI: https://doi.org/10.1007/978-1-4614-2185-6_34
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