Developing Inferential Estimation Algorithms using Genetic Programming

作     者：M.J. Willis H.G. Hiden G.A. Montague 

作者机构：Dept. Chemical & Process Engineering Newcastle University NEI 7RU United Kingdom 

出 版 物：《IFAC Proceedings Volumes》 

年 卷 期：1997年第30卷第9期

页      面：209-214页

主　　题：Estimation Algorithms Genetic Algorithms Modelling Extrusion Neural Networks 

摘      要：In this contribution, Genetic Programming (GP) is used to develop inferential estimation models using experimental data. GP performs symbolic optimisation, automatically determining both the structure and the complexity of an empirical model. After a tutorial example, the usefulness of the technique is demonstrated by the development of an inferential estimation model of a plasticating extruder. A statistical analysis procedure is used as a guide in the selection of the final model structure. For the industrial case study, the inferential models obtained using the GP algorithm are compared to those obtained using a linear, finite impulse response model and a feedforward artificial neural network (FANN). For this application, the GP technique produces models with a significantly lower Root Mean Square (RMS) error than the other techniques.