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Bi-directionalization of neural computing architecture for time series prediction. III. Application to laser intensity time record “Data Set A”

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2 Author(s)
H. Wakuya ; Dept. of Adv. Syst. Control Eng., Saga Univ., Japan ; K. Shida

For part II, see Int. Conf. on Dynamical Aspects in Complex Systems from Cells to Brain, p.43-4 (2000). One of the most important targets of time series prediction is an improvement of prediction quality for aiming at prefect prediction. To reach the goal, most studies have used uni-directional computation flow to predict future events from present and past information. In this study, on the contrary, bi-directional computation style is applied to a time series prediction task to investigate its effectiveness. As a result of computer simulations with the laser intensity time record “Data Set A”, it is clear that the coupling effect between the future and past prediction transformations produce a good advantage on trainability, generalization, and prediction quality over the conventional uni-directional network

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Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on  (Volume:3 )

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