Experimental investigation and optimal combustion control of untreated landfill gas via fuzzy logic rule knowledge based approach

2021 ◽  
Vol 121 ◽  
pp. 383-392
Author(s):  
Kanchit Pawananont ◽  
Thananchai Leephakpreeda
Keyword(s):  
Experimental Investigation ◽  
Fuzzy Logic ◽  
Landfill Gas ◽  
Combustion Control ◽  
Knowledge Based ◽  
Rule Knowledge ◽  
Logic Rule

scholarly journals Experts versus Algorithms? Optimized Fuzzy Logic Energy Management of Autonomous PV Hybrid Systems with Battery and H2 Storage

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1777
Author(s):  
Lisa Gerlach ◽  
Thilo Bocklisch
Keyword(s):  
Fuzzy Logic ◽  
Energy Management ◽  
Fuzzy Logic Controller ◽  
Expert Knowledge ◽  
Large Power ◽  
Hybrid Energy ◽  
Knowledge Based ◽  
Long Term Storage ◽  
Fuzzy Logic Approach

Off-grid applications based on intermittent solar power benefit greatly from hybrid energy storage systems consisting of a battery short-term and a hydrogen long-term storage path. An intelligent energy management is required to balance short-, intermediate- and long-term fluctuations in electricity demand and supply, while maximizing system efficiency and minimizing component stress. An energy management was developed that combines the benefits of an expert-knowledge based fuzzy logic approach with a metaheuristic particle swarm optimization. Unlike in most existing work, interpretability of the optimized fuzzy logic controller is maintained, allowing the expert to evaluate and adjust it if deemed necessary. The energy management was tested with 65 1-year household load datasets. It was shown that the expert tuned controller is more robust to changes in load pattern then the optimized controller. However, simple readjustments restore robustness, while largely retaining the benefits achieved through optimization. Nevertheless, it was demonstrated that there is no one-size-fits-all tuning. Especially, large power peaks on the demand-side require overly conservative tunings. This is not desirable in situations where such peaks can be avoided through other means.

Expert Knowledge-Based Traction Control of a Truck Using Fuzzy Logic

1996 ◽  
Vol 29 (1) ◽  
pp. 7867-7872
Author(s):  
Ka C. Cheok ◽  
Kazuyuki Kobayashi ◽  
Francis B. Hoogterp
Keyword(s):  
Fuzzy Logic ◽  
Expert Knowledge ◽  
Traction Control ◽  
Knowledge Based

scholarly journals An Application Expert System for Evaluating Effective Factors on Trust in B2C WebsitesTrust, Security, ANFIS, Fuzzy Logic, Rule Based Systems, Electronic Commerce

Engineering ◽  
2011 ◽  
Vol 03 (11) ◽  
pp. 1063-1071 ◽  
Author(s):  
Mehrbakhsh Nilashi ◽  
Karamollah Bagherifard ◽  
Othman Ibrahim ◽  
Nasim Janahmadi ◽  
Mousa Barisami
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Electronic Commerce ◽  
Rule Based ◽  
Effective Factors ◽  
Logic Rule ◽  
Rule Based Systems

scholarly journals Assessment of the Performance Status of a Wharf

Pomorstvo ◽  
2018 ◽  
Vol 31 (1) ◽  
pp. 245-257
Author(s):  
Pereowei Garrick Ombor ◽  
Thaddeus C. Nwaoha
Keyword(s):  
Fuzzy Logic ◽  
Statistical Data ◽  
Performance Status ◽  
Rule Base ◽  
Subjective Judgement ◽  
The Status ◽  
Base Method ◽  
Logic Rule ◽  
Good Agreement

This paper suggests Fuzzy-logic-rule base method to assess the performance status of a wharf in order to classify it. The method proposed is predicated upon its ability to analyse processes and operations based on subjective judgement with little or no statistical data available. The study also shows that fuzzy-logic rule base is a veritable tool to qualitatively and quantitatively assess the status of a wharf offering ferry service. Using the model developed in this study, the performance status of Yenagoa wharf has been determined to be orange, having a (WPS) overall of 4.8. The status of the wharf has been in good agreement with the perception of stakeholders that the Yenagoa wharf needs restructuring to curb the frequent crisis occurring among stakeholders. The model also indicated those areas of the wharf’s operations needing attention. The study indicates that even though major components that determine the quality and profitability of the wharf’s ferry service are high in value, the overall status of the wharf may not be necessarily high. As such, the study method can be used to control the growing ill-feeling between boat operators and passengers while harmonizing all stakeholders (operators, passengers, and regulators etc) to work together to improve the status of the wharf.

scholarly journals Knowledge Based Combustion Control for Slab Reheating Furnace

1987 ◽  
Vol 107 (8) ◽  
pp. 758-765
Author(s):  
Shinya Tanifuji ◽  
Yasuo Morooka ◽  
Junzo Nitta ◽  
Ichiro Maeda
Keyword(s):  
Combustion Control ◽  
Reheating Furnace ◽  
Knowledge Based

Fuzzy Sliding-Mode Control of a Variable Geometry Manipulator: Experimental Investigation

2000 ◽  
Author(s):  
J. Choi ◽  
C. W. de Silva ◽  
V. J. Modi ◽  
A. K. Misra
Keyword(s):  
Fuzzy Logic ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Tracking Error ◽  
Experimental Studies ◽  
Sliding Mode Controller ◽  
Fuzzy Sliding Mode Control ◽  
Mode Control ◽  
Knowledge Based ◽  
Fuzzy Sliding Mode

Abstract This paper focuses a robust and knowledge-based control approach for multi-link robot manipulator systems. Based on the concepts of sliding-mode control and fuzzy logic control (FLC), a fuzzy sliding-mode controller has been developed in previous work. This controller possesses good robustness properties of sliding-mode control and the flexibility and ‘intelligent’ capabilities of knowledge-based control through the use of fuzzy logic. This paper presents experimental studies with fuzzy sliding-mode control as well as conventional sliding-mode control. The results show that the tracking error is guaranteed to converge to a specification in the presence of uncertainties. The performance of the fuzzy sliding-mode controller is found to be somewhat better than that of the conventional sliding-mode controller.

Artificial Intelligence in Software Engineering

2012 ◽  
pp. 1215-1236 ◽  
Author(s):  
Farid Meziane ◽  
Sunil Vadera
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Software Engineering ◽  
Software Development ◽  
Language Processing ◽  
Knowledge Based ◽  
Development Life Cycle ◽  
Planning Methods ◽  
Software Development Life Cycle ◽  
Processing Techniques

Artificial intelligences techniques such as knowledge based systems, neural networks, fuzzy logic and data mining have been advocated by many researchers and developers as the way to improve many of the software development activities. As with many other disciplines, software development quality improves with the experience, knowledge of the developers, past projects and expertise. Software also evolves as it operates in changing and volatile environments. Hence, there is significant potential for using AI for improving all phases of the software development life cycle. This chapter provides a survey on the use of AI for software engineering that covers the main software development phases and AI methods such as natural language processing techniques, neural networks, genetic algorithms, fuzzy logic, ant colony optimization, and planning methods.

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