Time-series trend estimating system and method using column-stru

Details Time-series trend estimating system and method using column-stru Time-series trend estimating system and method using column-stru

1996-08-22

1999-09-21

Stamber, Eric W.

Data processing: artificial intelligence

Neural network

Learning task

706 20, 706 25, 706 19, 706 40, 706 34, 704250, G06F 1518

Patent

active

059567023

ABSTRACT:
Each neural element of a column-structured recurrent neural network generates an output from input data and recurrent data provided from a context layer of a corresponding column. One or more candidates for an estimated value is obtained, and an occurrence probability is computed using an internal state by solving an estimation equation determined by the internal state output from the neural network. A candidate having the highest occurrence probability is an estimated value for unknown data. Thus, the internal state of the recurrent neural network is explicitly associated with the estimated value for data, and a data change can be efficiently estimated.

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Jeng-Neng Hwang and Eric Little, "Real Time Recurrent Neural Networks for Time Series Prediction and Confidence Estimation", 1996 IEEE International Conference on Neural Networks p. 1889-94, Jun. 3-6, 1996.
C.R. Gent and C.P. Sheppard, "Predicting Time Series by a Fully Connected Neural Network Trained by Back Propagation" Computing and Control Engineering Journal, May 1992.

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