Performance analysis of stirling engine using computational intelligence techniques (ANN & Fuzzy Mamdani Model) and hybrid algorithms (ANN-PSO & ANFIS)

Stirling engine is considered as one of the most promising alternatives to conventional combustion units due to its versatility and potential to achieve relatively high efficiency. The output power and torque are the main performance indicators that depend on many variables. Many studies have pointe...

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Veröffentlicht in:	Neural computing & applications Jg. 35; H. 2; S. 1225 - 1245
Hauptverfasser:	Machesa, M. G. K., Tartibu, L. K., Okwu, M. O.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.01.2023 Springer Nature B.V
Schlagworte:	Adaptive algorithms Adaptive systems Algorithms Artificial Intelligence Artificial neural networks Business metrics Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Fuzzy logic Image Processing and Computer Vision Indicators Neural networks Parametric analysis Particle swarm optimization Performance prediction Probability and Statistics in Computer Science S.I. : 2019 India Intl. Congress on Computational Intelligence Soft computing Stirling engines Swarm intelligence Torque Artificial neural network (ANN) Stirling engine Particle swarm optimization (PSO) Fuzzy Mamdani model (FMM) Adaptive neuro-fuzzy inference system (ANFIS)
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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