Combining Statistics and Expert Systems with Neural Networks for Empirical Process Modeling


*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Referred Conference Paperpeer-review

7 Citations (Scopus)


Neural networks, statistical analysis, and expert systems have been successfully applied to a number of process industries for the purpose of process modeling and control. In most of the applications, it is often beneficial to integrate statistical methods with neural networks due to the similarity of neural network training and statistical modeling. The main focus of the paper is to combine statistical methods into neural network training in various steps including data pre-processing, variable selection, network training and testing, and post validation. The statistical methods addressed here are outlier detection, missing data replacement, partial least squares regression, principal component analysis, and cross-validation. A framework of using an expert system to guide the procedures to build a neural network is proposed.
Original languageEnglish
Title of host publicationAdvances in Instrumentation and Control : International Conference and Exhibition, 48 (pt 2)
Number of pages10
Publication statusPublished - Sept 1993
Externally publishedYes
EventISA/93 International Conference, Exhibition & Training Program - Chicago, United States
Duration: 19 Sept 199324 Sept 1993


ConferenceISA/93 International Conference, Exhibition & Training Program
Country/TerritoryUnited States


Dive into the research topics of 'Combining Statistics and Expert Systems with Neural Networks for Empirical Process Modeling'. Together they form a unique fingerprint.

Cite this