Neural networks in designing fuzzy systems for real world applications
Fuzzy Sets and Systems
The neural network model RuleNet and its application to mobile robot navigation
Fuzzy Sets and Systems - Special issue on methods for data analysis in classificatin and control
Discovering Statistics Using SPSS for Windows: Advanced Techniques for Beginners
Discovering Statistics Using SPSS for Windows: Advanced Techniques for Beginners
Expert Systems with Applications: An International Journal
Learning and tuning fuzzy logic controllers through reinforcements
IEEE Transactions on Neural Networks
On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm
IEEE Transactions on Neural Networks
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Improving the efficiency of the carbon dioxide (CO"2) capture process requires a good understanding of the intricate relationships among parameters involved in the process. The objective of this paper is to study the relationships among the significant parameters impacting CO"2 production. An enhanced understanding of the intricate relationships among the process parameters supports prediction and optimization, thereby improving efficiency of the CO"2 capture process. Our modeling study used the 3-year operational data collected from the amine-based post combustion CO"2 capture process system at the International Test Centre (ITC) of CO"2 Capture located in Regina, Saskatchewan of Canada. This paper describes the data modeling process using the approaches of (1) neural network modeling combined with sensitivity analysis and (2) neuro-fuzzy modeling technique. The results from the two modeling processes were compared from the perspectives of predictive accuracy, inclusion of parameters, and support for explication of problem space. We conclude from the study that the neuro-fuzzy modeling technique was able to achieve higher accuracy in predicting the CO"2 production rate than the combined approach of neural network modeling and sensitivity analysis.