Abstract
A tapped delay neural network (TDNN) with an adaptive learning and pruning algorithm is proposed to predict the nonlinear time serial stock indexes. The TDNN is trained by the recursive least square (RLS) in which the learning-rate parameter can be chosen automatically. This results in the network converging fast. Subsequently the architecture of the trained neural network is optimized by utilizing pruning algorithm to reduce the computational complexity and enhance the network’s generalization. And then the optimized network is retrained so that it has optimum parameters. At last the test samples are predicted by the ultimate network. The simulation and comparison show that this optimized neuron network model can not only reduce the calculating complexity greatly, but also improve the prediction precision. In our simulation, the computational complexity is reduced to 0.0556 and mean square error of test samples reaches 8.7961×10− 5.
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© 2007 Springer Berlin Heidelberg
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Shen, J., Fan, H., Chang, S. (2007). Stock Index Prediction Based on Adaptive Training and Pruning Algorithm. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_55
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DOI: https://doi.org/10.1007/978-3-540-72393-6_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72392-9
Online ISBN: 978-3-540-72393-6
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