NOx Emission Prediction of Waste Gas Based on the Grey Self-memory Bernoulli Model - GUO Xiaojun; ZHANG Rui; SHEN Houxue; YOU Dan (Nantong Daxue xuebao. Ziran kexue ban)
The traditional nonlinear grey Bernoulli model(NGBM) can describe the integral single-peaked evolution tendency of nonlinear systems with small samples; nevertheless, it cannot reveal the individual stochastic fluctuations due to plenty of uncertain external disturbances. There is a limitation of being sensitive to one initial value consisting in the differential equation of NGBM. To address this problem, a novel nonlinear grey self-memory Bernoulli coupling model( NGBSM) is introduced. The system ′s self-memorization equation in NGBSM can eliminate the drawback on account that it contains multiple time-points initial fields instead of only single time-point one. The case study of the NOxemission prediction of waste gas is carried out. The results show that the NGBSM model can adequately extract historical information from nonlinear systems and is much more efficient than the existing NGBM model, exponential smoothing(ES) and simple moving average(SMA). These findings can be applied to predict the individual stochastic fluctuations existing in other similar single-peaked nonlinear systems with small samples.