Applications of GRNN Based on Particle swarm algorithm Forecasting Stock Prices
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Generalized regression neural network (GRNN) has very good effect on making nonlinear forecasting model with large number of stock data. Particle swarm optimization (PSO) has simple operation analysis and is easy to implement. We use PSO algorithm to optimize the GRNN in order for optimal smoothing factor and connection weights. The prediction errors of the two models are both small. The MSE error by GRNN model reaches 0.0486, while the error by PSO-GRNN model is 0.0104. The analysis shows that PSO-GRNN model is more accurate, more stabilized and more generic than GRNN model.
- © 2013, the Authors. Published by Atlantis Press.
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Cite this article
TY - CONF AU - Jinna Lu AU - Yanping Bai PY - 2013/03 DA - 2013/03 TI - Applications of GRNN Based on Particle swarm algorithm Forecasting Stock Prices BT - Proceedings of the 2013 International Conference on Information, Business and Education Technology (ICIBET 2013) PB - Atlantis Press SP - 69 EP - 72 SN - 1951-6851 UR - https://doi.org/10.2991/icibet.2013.15 DO - https://doi.org/10.2991/icibet.2013.15 ID - Lu2013/03 ER -