Výsledky vyhľadávania - "Bayesian regulation backpropagation algorithm"

Scaled UKF-NARX hybrid model for multi-step-ahead forecasting of chaotic time series data.

Autori: Abdulkadir, Said Yong, Suet-Peng

Zdroj: Soft Computing - A Fusion of Foundations, Methodologies & Applications; Dec2015, Vol. 19 Issue 12, p3479-3496, 18p

Predmety: KALMAN filtering, ARTIFICIAL neural networks, AUTOREGRESSIVE models, BAYESIAN analysis, COMPUTER network architectures

Optimal artificial neural network design for simultaneous modeling of multicomponent adsorption.

Autori: Pauletto, Paola S. Dotto, Guilherme L. Salau, Nina P.G.

Zdroj: Journal of Molecular Liquids. Dec2020:Part A, Vol. 320, pN.PAG-N.PAG. 1p.

Predmety: *ARTIFICIAL neural networks, *ADSORBATES, *LANGMUIR isotherms, *ADSORPTION isotherms, *ADSORPTION (Chemistry), *ADSORPTION capacity, *METHYLENE blue

Three-dimensional graphene network supported nickel-cobalt bimetallic alloy nanocatalyst for hydrogen production by hydrolysis of sodium borohydride and developing of an artificial neural network modeling to forecast hydrogen production rate.

Autori: Karaman, Onur¹ (AUTHOR) onurkaraman@akdeniz.edu.tr

Zdroj: Chemical Engineering Research & Design: Transactions of the Institution of Chemical Engineers Part A. May2022, Vol. 181, p321-330. 10p.

Predmety: *SODIUM borohydride, *INTERSTITIAL hydrogen generation, *ARTIFICIAL neural networks, *HYDROGEN production, *SOCIAL networks, *HYDROLYSIS

Artificial neural network based chaotic generator for cryptology.

Autori: DALKIRAN, İlker DANIŞMAN, Kenan

Zdroj: Turkish Journal of Electrical Engineering & Computer Sciences; 2010, Vol. 18 Issue 2, p225-240, 16p, 2 Black and White Photographs, 6 Diagrams, 4 Charts, 4 Graphs

Predmety: CRYPTOGRAPHY, ARTIFICIAL neural networks, CHAOS theory, ALGORITHMS, CASE studies

ANN‐Enhanced Energy Reference Models for Industrial Buildings: Multinational Company Case Study.

Autori: Ouaomar, Younes Benkechcha, Said Kaddiri, Mourad a ďalší

Zdroj: Modelling & Simulation in Engineering; 8/7/2024, Vol. 2024, p1-15, 15p

Predmety: REGRESSION analysis, PARTIAL least squares regression, ARTIFICIAL neural networks, NONLINEAR regression, WASTE minimization

A Review on Membrane Fouling Prediction Using Artificial Neural Networks (ANNs).

Autori: Abuwatfa, Waad H. AlSawaftah, Nour Darwish, Naif a ďalší

Zdroj: Membranes; Jul2023, Vol. 13 Issue 7, p685, 29p

Artificial intelligence based prediction models for rubber compounds.

Autori: Uruk, Zeynep Kiraz, Alper

Zdroj: Journal of Polymer Engineering; Feb2023, Vol. 43 Issue 2, p113-124, 12p

Predmety: RUBBER, ARTIFICIAL intelligence, KRIGING, PREDICTION models, VULCANIZATION, RUBBER industry, ARTIFICIAL neural networks

Real-Time Estimation Frameworks for Feeder-Level Load Disaggregation and PEVs’ Charging Behavior Characteristics Extraction.

Autori: Ebrahimi, Mehrdad Rastegar, Mohammad Arefi, Mohammad Mehdi

Zdroj: IEEE Transactions on Industrial Informatics; Jul2022, Vol. 18 Issue 7, p4715-4724, 10p

Evaluation of lubricant condition and engine health based on soft computing methods.

Autori: Pourramezan, Mohammad-Reza Rohani, Abbas Keramat Siavash, Nemat a ďalší

Zdroj: Neural Computing & Applications; Apr2022, Vol. 34 Issue 7, p5465-5477, 13p

Predmety: SOFT computing, MECHANICAL engineering, ENGINES, IRON, MECHANICAL engineers, DIESEL multiple units

A Bayesian regularized feed-forward neural network model for conductivity prediction of PS/MWCNT nanocomposite film coatings.

Autori: Demirbay, Barış Kara, Duygu Bayram Uğur, Şaziye

Zdroj: Applied Soft Computing. Nov2020, Vol. 96, pN.PAG-N.PAG. 1p.

Predmety: Artificial neural networks, Composite membranes (Chemistry), Standard & Poor's 500 Index, Electric conductivity, Prediction models, Molecular weights

Investigation of relationship between resistance spot welding condition and nugget shape by utilizing machine learning based technique.

Autori: Kitano, Houichi Sato, Akira Iyota, Muneyoshi a ďalší

Zdroj: Welding International; Apr-Jun2019, Vol. 33 Issue 4-6, p223-230, 8p

Predmety: SPOT welding, MACHINE learning, IRON & steel plates

Index.

Autori: Otto, Matthias

Zdroj: Chemometrics; 2016, p371-383, 13p

Knowledge Processing and Soft Computing.

Autori: Otto, Matthias

Zdroj: Chemometrics; 2016, p297-344, 48p

Comparison of sigmoidal FFANN training algorithms for function approximation problems.

Autori: Bhatia, MPS Veenu Chandra, Pravin

Zdroj: 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom); 2015, p717-721, 5p

Hand movement classification using transient state analysis of surface multichannel EMG signal.

Autori: Mobarak, M. Pla Guerrero, R. Munoz Gutierrez Salgado, J. M. a ďalší

Zdroj: 2014 Pan American Health Care Exchanges (PAHCE); 2014, p1-6, 6p

Multicriteria approaches for predictive model generation: A comparative experimental study.

Autori: Al-Jubouri, Bassma Gabrys, Bogdan

Zdroj: 2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM); 2014, p64-71, 8p

Studying the abrasion behavior of rubbery materials with combined design of experiment-artificial neural network.

Autori: Shiva, Mehdi Atashi, Hossein Hassanpourfard, Mahtab

Zdroj: Chinese Journal of Polymer Science (Springer Science & Business Media B.V.); Jul2012, Vol. 30 Issue 4, p520-529, 10p

Predmety: ARTIFICIAL neural networks, RUBBER, RESPONSE surfaces (Statistics), ABRASION resistance, ARTIFICIAL intelligence

Reduction of Stimulus Artifact in Somatosensory Evoked Potentials: Segmented Versus Subthreshold Training.

Autori: Boudreau, Brigitte H. Englehart, Kevin B. Chan, Adrian D. C. a ďalší

Zdroj: IEEE Transactions on Biomedical Engineering; Jul2004, Vol. 51 Issue 7, p1187-1195, 9p

Predmety: SOMATOSENSORY evoked potentials, ARTIFICIAL neural networks, ATRIAL arrhythmias, CATHODE ray oscillographs, ELECTROPHYSIOLOGY, ARTIFICIAL intelligence

A Bayesian regularized feed-forward neural network model for conductivity prediction of PS/MWCNT nanocomposite film coatings

Autori: Barış Demirbay Duygu Bayram Kara Şaziye Uğur

Zdroj: Applied Soft Computing. 96:106632

Predmety: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 0210 nano-technology

Prístupová URL adresa: https://dblp.uni-trier.de/db/journals/asc/asc96.html#DemirbayKU20
https://www.sciencedirect.com/science/article/pii/S1568494620305706
https://www.sciencedirect.com/science/article/pii/S1568494620305706#!

Process Parameter Optimization Framework for the Selective Laser Melting of Hastelloy X Alloy Considering Defects and Solidification Crack Occurrence.

Autori: Kitano, Houichi Kusano, Masahiro Tsujii, Masakazu a ďalší

Zdroj: Crystals (2073-4352); Jun2021, Vol. 11 Issue 6, p578-578, 1p

Predmety: SELECTIVE laser melting, PROCESS optimization, SOLIDIFICATION, ALLOYS, THERMAL analysis