Two States Mapping Based Time Series Neural Network Model for Compensation Prediction Residual Error
Insung Jung, Lock-Jo Koo, Gi-Nam Wang, Theodore E. Simos, George Psihoyios · AIP conference proceedings · 2008
The objective of this paper was to design a model of human bio signal data prediction system for decreasing of prediction error using two states mapping based time series neural network BP (back‐propagation) model. Normally, a lot of the industry has been applied neural network model by training them in a supervised manner with the error back‐propagation algorithm for time series prediction systems. However, it still has got a residual error between real value and prediction result. Therefore, we designed two states of neural network model for compensation residual error which is possible to use in the prevention of sudden death and metabolic syndrome disease such as hypertension disease and obesity. We determined that most of the simulation cases were satisfied by the two states mapping based time series prediction model. In particular, small sample size of times series were more accurate than the standard MLP model.