Determining optimal quality distribution of latex weight using adaptive neuro-fuzzy modeling and control systems

Authors:
Osman Taylan;Ibrahim A. Darrab
Affiliations:
King Abdulaziz University, College of Engineering, Industrial Engineering Department, P.O. Box 80204, Jeddah 21589, Saudi Arabia;King Abdulaziz University, College of Engineering, Industrial Engineering Department, P.O. Box 80204, Jeddah 21589, Saudi Arabia
Venue:
Computers and Industrial Engineering
Year:
2011

Citing 12
Cited 0

Fuzzy mathematical techniques with applications

Fuzzy mathematical techniques with applications
Neural-Network-Based Fuzzy Logic Control and Decision System

IEEE Transactions on Computers - Special issue on artificial neural networks
Fuzzy Control and Fuzzy Systems

Fuzzy Control and Fuzzy Systems
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing

Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
Fuzzy Mathematical Programming: Methods and Applications

Fuzzy Mathematical Programming: Methods and Applications
Estimating and testing process yield with imprecise data

Expert Systems with Applications: An International Journal
Survey paper: Computer-Aided Inspection Planning-The state of the art

Computers in Industry
A neural fuzzy system with fuzzy supervised learning

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Performance evaluation of fuzzy classifier systems formultidimensional pattern classification problems

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Design of fuzzy systems using neurofuzzy networks

IEEE Transactions on Neural Networks
Neuro-fuzzy rule generation: survey in soft computing framework

IEEE Transactions on Neural Networks
Learning and tuning fuzzy logic controllers through reinforcements

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper introduces a systematic approach for the design of an adaptive neuro-fuzzy inference system (ANFIS) for latex weight control of level loop carpets. In high production volume of some industries, manual control could lead to undesirable variations in product quality. Therefore, process parameters require continuous checking and testing against quality standards. One way to overcome this problem is to use statistical process control by which a complete elimination of variability may not be possible. Fuzzy logic (FL) control is one of the most significant applications of fuzzy logic and fuzzy set theory. Fuzzy if-then rules (controllers) were developed in a systematic way that formed the backbone of the neuro-fuzzy control system. The developed ANFIS was able to produce crisp numerical outcomes to predict latex weights. The neuro-fuzzy system behaved like human operators. ANFIS outcomes were encouraging because they provide a more efficient and uniform distribution of latex weight and seemed to be better than the other statistical process control tools. FL controllers provide a feasible alternative to capture approximate, qualitative aspects of human reasoning and decision making processes.