> Detailanzeige

Zhang, Xiaoluan [VerfasserIn]; Liu, Xinning [VerfasserIn]; Wang, Xifeng [VerfasserIn]; Band, Shahab [VerfasserIn]; Bagherzadeh, Seyed Amin [VerfasserIn]; Taherifar, Somaye [VerfasserIn]; Abdollahi, Ali [VerfasserIn]; Bahrami, Mehrdad [VerfasserIn]; Karimipour, Arash [VerfasserIn]; Chau, Kwok-Wing [VerfasserIn]; Mosavi, Amir [VerfasserIn]

Energetic thermo-physical analysis of MLP-RBF feed-forward neural network compared with RLS Fuzzy to predict CuO/liquid paraffin mixture properties

Literatur-
verwaltung

Zur
Merkliste

Lösche von
Merkliste

Per Whatsapp teilen

Schließen

Merkliste



Sie können Bookmarks mittels Listen verwalten, loggen Sie sich dafür bitte in Ihr SLUB Benutzerkonto ein.
  • Medientyp: E-Book
  • Titel: Energetic thermo-physical analysis of MLP-RBF feed-forward neural network compared with RLS Fuzzy to predict CuO/liquid paraffin mixture properties
  • Beteiligte: Zhang, Xiaoluan [VerfasserIn]; Liu, Xinning [VerfasserIn]; Wang, Xifeng [VerfasserIn]; Band, Shahab [VerfasserIn]; Bagherzadeh, Seyed Amin [VerfasserIn]; Taherifar, Somaye [VerfasserIn]; Abdollahi, Ali [VerfasserIn]; Bahrami, Mehrdad [VerfasserIn]; Karimipour, Arash [VerfasserIn]; Chau, Kwok-Wing [VerfasserIn]; Mosavi, Amir [VerfasserIn]
  • Erschienen: [S.l.]: SSRN, [2022]
  • Umfang: 1 Online-Ressource (40 p)
  • Sprache: Englisch
  • Entstehung:
  • Anmerkungen: In: Xiaoluan Zhang, Xinni Liu, Xifeng Wang, Shahab S. Band, Seyed Amin Bagherzadeh, Somaye Taherifar, Ali Abdollahi, Mehrdad Bahrami, Arash Karimipour, Kwok-Wing Chau & Amir Mosavi (2022) Energetic thermo-physical analysis of MLP-RBF feed-forward neural network compared with RLS Fuzzy to predict CuO/li
    Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments April 12, 2022 erstellt
  • Beschreibung: Dynamic viscosity of novel generated Copper Oxide (CuO)/Liquid Paraffin nanofluids is obtained experimentally for various temperatures and concentrations. To optimize the empirical process and for cost-efficiency, Feed-Forward Neural Networks (FFNNs) were modeled and compared with Recursive Least Squares (RLS) Fuzzy model. To prepare CuO/ liquid paraffin nanofluids, CuO nanoparticles are dispersed within paraffin. Based on the empirical results, two types of FFNNs are examined and compared with RLSF model to predict CuO/liquid paraffin nanofluids. To evaluate the best optimization methods of nanofluid viscosity, Multi-Layer Feed forward (MLF), Radial Basis Function (RBF), and RLSF are compared and discussed. The MLF network provides a global approximation while the RBF acts more locally, further, RLSF provides a better fit. On the contrary, the RBF network has better properties from the generalization and noise rejection points of view. Also, RBF networks can be applied in an online manner. Further, three curves of RLS Fuzzy model by Parabola2D, ExtremeCum, and Poly2D models were fitted on the empirical data and compared. The ExtremeCum model showed the least margin of error and can be employed to predict the data
  • Zugangsstatus: Freier Zugang