关键词:

神经网络集成, 选择性集成, 模型差异度, 溶剂脱水塔

Abstract:

Neural network ensemble could dramatically improve the generalization performance of neural network.In traditional ensemble processes, all the trained networks are directly combined to the integrated networkHowever, these networks may have certain correlation in fact.Therefore, selective neural network ensemble has become a hot issue recently, by which the generalization ability of neural network ensemble can be further improved.Thus, the authors propose a new selective constructing approach to neural network ensemble named DWSEN through measuring the diversity of individuals according to weights of networks.Compared with some prevailing ensemble approaches such as Bagging and Boosting, testing of UCI data sets illustrated that the DWSEN approach has higher generalization ability and stronger stability.This method is further validated by the modeling of solvent dehydration tower of purified terephthalic acid (PTA) solvent system.Case study shows that the obtained model has better generalization performance, and can simulate the production process better.

Key words:

神经网络集成, 选择性集成, 模型差异度, 溶剂脱水塔