Model Recognition Orientated at Small Sample Data

System modeling is a prerequisite to understand object properties, while the chances are that an industrial site may come along with restricted conditions, leading to less experimental data acquired about the objects. In this case, the application of traditional statistical law of large numbers for modeling certainly will influence the identification precision. Aiming at the problems in recognition of small samples being not that high in precision, it is proposed to introduce the bootstrap-based re-sampling technique, upon which the original small sample data are expanded in order to meet the requirements of the statistical recognition method for sample quantity, so as to meet the requirements of precision. The simulation results showed that the extended sample model recognition accuracy is substantially higher than that of the original small sample. This illustrates the validity of the bootstrap-based re-sampling technique, working as an effective way for small sample data processing.