A novel NN-Fuzzy-SPC feedback control system

Li Ren Wang*, Hefin Rowlands

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)


It is a difficult challenge to develop a feedback control system for Statistical Process Control (SPC) because there is no effective method that can be used to accurately calculate the magnitude of the feedback control actions in traditional SPC. Suitable feedback adjustments are normally generated from the experiences of process engineers. In this paper, fuzzy logic and neural network (NN) techniques are used to develop a NN-Fuzzy-SPC control system. The fuzzy inference is used to generate the numeric feedback control actions and the neural network optimises the fuzzy membership functions in order to increase control accuracy. A combined forecaster with EWMA chart and digital filtering is also developed for the NN-Fuzzy-SPC system to reduce the control delay. Simulation results show that the NN-Fuzzy-SPC system can provide high control accuracy and satisfactorily short control delay.

Original languageEnglish
Title of host publicationIEEE International Conference on Emerging Technologies and Factory Automation, ETFA
PublisherInstitute of Electrical and Electronics Engineers
Number of pages7
ISBN (Print)0-7803-7241-7
Publication statusPublished - 2001
Externally publishedYes
Event8th International Conference on Emerging Technologies and Factory Automation (ETFA 2001) - Antibes-Juan les Pins, France
Duration: 15 Oct 200118 Oct 2001


Conference8th International Conference on Emerging Technologies and Factory Automation (ETFA 2001)
CityAntibes-Juan les Pins


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