An accurate signal estimator using a novel smart adaptive grey model SAGM(1,1)

摘要

Grey system theory has been developed for almost 30 years and has obtained many great successes in practical real-life applications. However, conventional grey models show some limitations which affect directly to the model applicability as well as prediction accuracy. Hence, the aim of this paper is a proposition of a novel grey model named ‘Smart Adaptive Grey Model’ – SAGM(1,1) in order to overcome the disadvantages existing in the original grey model – GM(1,1). The proposed model was developed from the GM(1,1) model with three remarkable improvements. The first one is a use of two smartly additive factors to convert any raw data into a grey sequence which satisfies both the raw data checking condition and quasi-smooth condition to perform the grey estimation. The second one is a modification in calculating the background series which affect to the grey model accuracy. And the final improvement is a modification in computing the model output by using a so-called error correcting accumulation (ECA) to eliminate the residual prediction errors. As a result, the SAGM(1,1) model can be applied easily to any practical prediction problem and achieve higher prediction accuracy comparing with the conventional GM(1,1) model. Numerical simulations have been carried out to verify the proposed model.