Residue Amending Combined BP Prediction Model

Cyber-Enabled Distributed Computing and Knowledge Discovery（2010）

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Abstract

The thesis introduces grey system model and BP neural network. Through making full use of the merits of GM(1.1) and neural network model and overcoming their drawbacks, we construct the grey residue amending combined and prediction model based on BP Neural network, and such combined model as “combined prediction model= tendency prediction model/GM(1.1)+neural network model”, and makes a contrast between the three models in prediction and precision. The result indicates that, the combined model is better than that of the single models for higher precision and smaller error.

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Key words

bp neural network,bp prediction model,neural network model,grey systems,combined prediction model,higher precision,tendency prediction model,single model,residue amending,combined model,backpropagation,grey system model,bp neural network model,residue amending combined bp,grey model,prediction theory,grey residue,prediction model,neural nets,inspection,mathematical model,network model,petroleum,predictive models,system modeling,neural network,data models,artificial neural networks

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