{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T11:00:35Z","timestamp":1777374035235,"version":"3.51.4"},"reference-count":67,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,7,2]],"date-time":"2021-07-02T00:00:00Z","timestamp":1625184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["433052568"],"award-info":[{"award-number":["433052568"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Scale deposits can reduce equipment efficiency in the oil and petrochemical industry. The gamma attenuation technique can be used as a non-invasive effective tool for detecting scale deposits in petroleum pipelines. The goal of this study is to propose a dual-energy gamma attenuation method with radial basis function neural network (RBFNN) to determine scale thickness in petroleum pipelines in which two-phase flows with different symmetrical flow regimes and void fractions exist. The detection system consists of a dual-energy gamma source, with Ba-133 and Cs-137 radioisotopes and two 2.54-cm \u00d7 2.54-cm sodium iodide (NaI) detectors to record photons. The first detector related to transmitted photons, and the second one to scattered photons. The transmission detector recorded two signals, which were the counts under photopeak of Ba-133 and Cs-137 with the energy of 356 keV and 662 keV, respectively. The one signal recorded in the scattering detector, total counts, was applied to RBFNN as the inputs, and scale thickness was assigned as the output.<\/jats:p>","DOI":"10.3390\/sym13071198","type":"journal-article","created":{"date-parts":[[2021,7,2]],"date-time":"2021-07-02T10:06:34Z","timestamp":1625220394000},"page":"1198","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Application of Gamma Attenuation Technique and Artificial Intelligence to Detect Scale Thickness in Pipelines in Which Two-Phase Flows with Different Flow Regimes and Void Fractions Exist"],"prefix":"10.3390","volume":"13","author":[{"given":"Mohammed","family":"Alamoudi","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah 21589, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad Amir","family":"Sattari","sequence":"additional","affiliation":[{"name":"Friedrich Schiller University Jena, F\u00fcrstengraben 1, 07743 Jena, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7978-401X","authenticated-orcid":false,"given":"Mohammed","family":"Balubaid","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah 21589, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1480-1450","authenticated-orcid":false,"given":"Ehsan","family":"Eftekhari-Zadeh","sequence":"additional","affiliation":[{"name":"Institute of Optics and Quantum Electronics, Friedrich-Schiller-University Jena, Max-Wien-Platz 1, 07743 Jena, Germany"},{"name":"Helmholtz Institute Jena, Fr\u00f6belstieg 3, 07743 Jena, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5457-6943","authenticated-orcid":false,"given":"Ehsan","family":"Nazemi","sequence":"additional","affiliation":[{"name":"Imec-Vision Lab, Department of Physics, University of Antwerp, 2610 Antwerp, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5806-3237","authenticated-orcid":false,"given":"Osman","family":"Taylan","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, P.O. 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Energy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.petrol.2012.04.005","article-title":"Inhibition of barium sulfate scale at high-barium formation water","volume":"90","author":"BinMerdhah","year":"2012","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1016\/j.petrol.2011.08.007","article-title":"Artificial neural network for permeability damage prediction due to sulfate scaling","volume":"78","author":"Zabihi","year":"2011","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/j.radphyschem.2013.03.007","article-title":"Scale analysis using X-ray microfluorescence and computed radiography","volume":"95","author":"Candeias","year":"2014","journal-title":"Radiat. Phys. Chem."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.apradiso.2019.06.019","article-title":"Characterization of scale deposition in oil pipelines through X-Ray Microfluorescence and X-Ray microtomography","volume":"151","author":"Oliveira","year":"2019","journal-title":"Appl. Radiat. Isot."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.apradiso.2013.01.012","article-title":"Determination of wax deposition and corrosion in pipelines by neutron back diffusion collimation and neutron capture gamma rays","volume":"74","year":"2013","journal-title":"Appl. Radiat. Isot."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1016\/j.nima.2014.11.030","article-title":"Gamma transmission system for detection of scale in oil exploration pipelines","volume":"784","author":"Oliveira","year":"2015","journal-title":"Nucl. Instrum. Methods Phys. Res. Sect. A Accel. Spectrometers Detect. Assoc. Equip."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.apradiso.2018.08.008","article-title":"Inorganic scale thickness prediction in oil pipelines by gamma-ray attenuation and artificial neural network","volume":"141","author":"Teixeira","year":"2018","journal-title":"Appl. Radiat. Isot."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.1016\/S0895-4356(96)00002-9","article-title":"Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes","volume":"49","author":"Tu","year":"1996","journal-title":"J. Clin. Epidemiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"108549","DOI":"10.1016\/j.radphyschem.2019.108549","article-title":"Application of artificial intelligence in scale thickness prediction on offshore petroleum using a gamma-ray densitometer","volume":"168","author":"Salgado","year":"2020","journal-title":"Radiat. Phys. Chem."},{"key":"ref_11","unstructured":"Pelowitz, D.B. (2005). MCNP-X TM User\u2019s Manual, Version 2.5.0, Los Alamos National Laboratory. LA-CP-05e0369."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"113","DOI":"10.2478\/johh-2018-0039","article-title":"Experimental investigation of fine-grained settling slurry flow behaviour in inclined pipe sections","volume":"67","author":"Kesely","year":"2019","journal-title":"J. Hydrol. Hydromech."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"108427","DOI":"10.1016\/j.measurement.2020.108427","article-title":"Combination of X-ray tube and GMDH neural network as a nondestructive and potential technique for measuring characteristics of gas-oil\u2013water three phase flows","volume":"168","author":"Roshani","year":"2021","journal-title":"Measurement"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Mosorov, V., Zych, M., Hanus, R., Sankowski, D., and Saoud, A. (2020). Improvement of flow velocity measurement algorithms based on correlation function and twin plane electrical capacitance tomography. Sensors, 20.","DOI":"10.3390\/s20010306"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.apradiso.2016.08.001","article-title":"Density prediction for petroleum and derivatives by gamma-ray attenuation and artificial neural networks","volume":"116","author":"Salgado","year":"2016","journal-title":"Appl. Radiat. Isot."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1955","DOI":"10.1016\/j.aej.2020.11.043","article-title":"Evaluation of flow pattern recognition and void fraction measurement in two phase flow independent of oil pipeline\u2019s scale layer thickness","volume":"60","author":"Roshani","year":"2021","journal-title":"Alex. Eng. J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"108474","DOI":"10.1016\/j.measurement.2020.108474","article-title":"Applicability of time-domain feature extraction methods and artificial intelligence in two-phase flow meters based on gamma-ray absorption technique","volume":"168","author":"Sattari","year":"2021","journal-title":"Measurement"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.flowmeasinst.2018.03.006","article-title":"Density and velocity determination for single-phase flow based on radiotracer technique and neural networks","volume":"61","author":"Roshani","year":"2018","journal-title":"Flow Meas. Instrum."},{"key":"ref_19","first-page":"145","article-title":"Evaluation of liquid-gas flow in pipeline using gamma-ray absorption technique and advanced signal processing","volume":"28","author":"Hanus","year":"2021","journal-title":"Metrol. Meas. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"83","DOI":"10.2478\/johh-2019-0023","article-title":"Concentration distribution and deposition limit of medium-coarse sand-water slurry in inclined pipe","volume":"68","author":"Kesely","year":"2020","journal-title":"J. Hydrol. Hydromech."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.apradiso.2016.03.029","article-title":"Modelling of dynamic experiments in MCNP5 environment","volume":"112","author":"Mosorov","year":"2016","journal-title":"Appl. Radiat. Isot."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.nima.2013.09.047","article-title":"Intercomparison of gamma ray scattering and transmission techniques for gas volume fraction measurements in two phase pipe flow","volume":"735","year":"2014","journal-title":"Nucl. Instrum. Methods Phys. Res. A"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Mosorov, V., Rybak, G., and Sankowski, D. (2021). Plug regime flow velocity measurement problem based on correlability notion and twin plane electrical capacitance tomography: Use case. Sensors, 21.","DOI":"10.3390\/s21062189"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/S0955-5986(98)00043-0","article-title":"Improved void fraction determination by means of multibeam gamma-ray attenuation measurements","volume":"10","author":"Abro","year":"1999","journal-title":"Flow Meas. Instrum."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Roshani, M., Phan, G., Faraj, R.H., Phan, N.-H., Roshani, G.H., Nazemi, B., Corniani, E., and Nazemi, E. (2021). Proposing a gamma ra-diation based intelligent system for simultaneous analyzing and detecting type and amount of petroleum by-products. Nucl. Eng. Technol.","DOI":"10.1016\/j.net.2020.09.015"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1016\/j.radphyschem.2013.03.025","article-title":"Tomographic segmentation in multiphase flow measurement","volume":"95","author":"Tjugum","year":"2014","journal-title":"Radiat. Phys. Chem."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"109552","DOI":"10.1016\/j.apradiso.2020.109552","article-title":"Optimization of a flow regime identification system and prediction of volume fractions in three-phase systems using gamma-rays and artificial neural network","volume":"169","author":"Salgado","year":"2021","journal-title":"Appl. Radiat. Isot."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.flowmeasinst.2018.02.008","article-title":"Identification of liquid-gas flow regime in a pipeline using gamma-ray absorption technique and computational intelligence methods","volume":"60","author":"Hanus","year":"2018","journal-title":"Flow Meas. Instrum."},{"key":"ref_29","first-page":"241","article-title":"Analysis of CHF in saturated forced convective boiling on a heated surface with impinging jets using artificial neural network and genetic algorithm","volume":"9","author":"Cong","year":"2011","journal-title":"Nucl. Eng. Des."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1016\/j.pnucene.2010.02.001","article-title":"Flow regime identification and volume fraction prediction in multiphase flows by means of gamma-ray attenuation and artificial neural networks","volume":"52","author":"Salgado","year":"2010","journal-title":"Prog. Nucl. Energy"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"109784","DOI":"10.1016\/j.apradiso.2021.109784","article-title":"Development of a non-invasive method for monitoring variations in salt concentrations of seawater using nuclear technique and Monte Carlo simulation","volume":"174","author":"Barbosa","year":"2021","journal-title":"Appl. Radiat. Isot."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"107851","DOI":"10.1016\/j.measurement.2020.107851","article-title":"Uncertainty of mass flow measurement using centric and eccentric orifice for Reynolds number in the range 10,000 \u2264 Re \u2264 20,000","volume":"160","author":"Mrowiec","year":"2020","journal-title":"Measurement"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3619","DOI":"10.1007\/s00521-018-3673-0","article-title":"Investigation of different sources in order to optimize the nuclear metering system of gas\u2013oil\u2013water annular flows","volume":"32","author":"Karami","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"101804","DOI":"10.1016\/j.flowmeasinst.2020.101804","article-title":"Application of GMDH neural network technique to improve measuring precision of a simplified photon attenuation based two-phase flowmeter","volume":"75","author":"Roshani","year":"2020","journal-title":"Flow Meas. Instrum."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Rodriguez-Eguia, I., Errasti, I., Fernandez-Gamiz, U., Blanco, J.M., Zulueta, E., and Saenz-Aguirre, A. (2020). A parametric study of trailing edge flap implementation on three different airfoils through an artificial neuronal network. Symmetry, 12.","DOI":"10.3390\/sym12050828"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Moradi, M.J., Roshani, M.M., Shabani, A., and Kioumarsi, M. (2020). Prediction of the load-bearing behavior of spsw with rectangular opening by RBF net-work. Appl. Sci., 10.","DOI":"10.3390\/app10031185"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"700","DOI":"10.1140\/epjp\/s13360-021-01623-5","article-title":"A novel metaheuristic combinatorial algorithm to optimize the natural convection across a vertical enclosure divided by perforated flat horizontal louvers inside","volume":"136","author":"Karami","year":"2021","journal-title":"Eur. Phys. J. Plus"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"109581","DOI":"10.1109\/ACCESS.2020.3001973","article-title":"Artificial intelligence and COVID-19: Deep learning approaches for diagnosis and treatment","volume":"8","author":"Jamshidi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Xue, H., Yu, P., Zhang, M., Zhang, H., Wang, E., Wu, G., Li, Y., and Zheng, X. (2021). A wet gas metering system based on the extended-throat venturi tube. Sensors, 21.","DOI":"10.3390\/s21062120"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"106991","DOI":"10.1016\/j.measurement.2019.106991","article-title":"Design of a high efficiency class-F power amplifier with large signal and small signal measurements","volume":"149","author":"Roshani","year":"2020","journal-title":"Measurement"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s00170-013-5180-x","article-title":"Combined effect of TiO2 nanoparticles and input welding parameters on the weld bead penetration in submerged arc welding process using fuzzy logic","volume":"70","author":"Aghakhani","year":"2014","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-021-87477-4","article-title":"Size reduction and performance improvement of a microstrip Wilkinson power divider using a hybrid design technique","volume":"11","author":"Jamshidi","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Di Nunno, F., Alves Pereira, F., de Marinis, G., Di Felice, F., Gargano, R., Miozzi, M., and Granata, F. (2020). Deformation of air bubbles near a plunging jet using a machine learning approach. Appl. Sci., 10.","DOI":"10.3390\/app10113879"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.aeue.2018.10.022","article-title":"A modified class-F power amplifier with miniaturized harmonic control circuit","volume":"97","author":"Pirasteh","year":"2018","journal-title":"AEU Int. J. Electron. Commun."},{"key":"ref_45","first-page":"3126","article-title":"Design of a miniaturized planar microstrip Wilkinson power divider with harmonic cancellation","volume":"28","author":"Lotfi","year":"2020","journal-title":"Turk. J. Electr. Eng. Comput. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1109\/LCA.2020.3023723","article-title":"GPU-NEST: Characterizing energy efficiency of multi-GPU inference servers","volume":"19","author":"Jahanshahi","year":"2020","journal-title":"IEEE Comput. Archit. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Khaleghi, M., Salimi, J., Farhangi, V., Moradi, M.J., and Karakouzian, M. (2021). Application of artificial neural network to predict load bearing capacity and stiffness of perforated masonry walls. CivilEng, 2.","DOI":"10.3390\/civileng2010004"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1736","DOI":"10.3906\/elk-1801-127","article-title":"Compact microstrip lowpass filter with ultrasharp response using a square-loaded modified T-shaped resonator","volume":"26","author":"Pirasteh","year":"2018","journal-title":"Turk. J. Electr. Eng. Comput. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Arief, H.A., Wiktorski, T., and Thomas, P.J. (2021). A survey on distributed fibre optic sensor data modelling techniques and machine learning algorithms for multiphase fluid flow estimation. Sensors, 21.","DOI":"10.3390\/s21082801"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Jahanshahi, A., Taram, M.K., and Eskandari, N. (, January October). Blokus duo game on FPGA. Proceedings of the 17th CSI International Symposium on Computer Architecture & Digital Systems (CADS 2013).","DOI":"10.1109\/CADS.2013.6714256"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Rushd, S., Hafsa, N., Al-Faiad, M., and Arifuzzaman, M. (2021). Modeling the settling velocity of a sphere in Newtonian and non-Newtonian fluids with machine-learning algorithms. Symmetry, 13.","DOI":"10.3390\/sym13010071"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Moradi, M.J., and Hariri-Ardebili, M.A. (2019). Developing a library of shear walls database and the neural network based predictive meta-model. Appl. Sci., 9.","DOI":"10.3390\/app9122562"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Roshani, S., Jamshidi, M.B., Mohebi, F., and Roshani, S. (2020). Design and modeling of a compact power divider with squared resonators using artificial intelligence. Wirel. Pers. Commun.","DOI":"10.1007\/s11277-020-07960-5"},{"key":"ref_54","unstructured":"Jahanshahi, A. (2019). TinyCNN: A tiny modular CNN accelerator for embedded FPGA. arXiv."},{"key":"ref_55","first-page":"1042","article-title":"Two-section impedance transformer design and modeling for power amplifier applications","volume":"32","author":"Roshani","year":"2017","journal-title":"Appl. Comput. Electromagn. Soc. J."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"105373","DOI":"10.1016\/j.icheatmasstransfer.2021.105373","article-title":"Velocity prediction of Cu\/water nanofluid convective flow in a circular tube: Learning CFD data by differential evolution algorithm based fuzzy inference system (DEFIS)","volume":"126","author":"Nabavi","year":"2021","journal-title":"Int. Commun. Heat Mass Transf."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Pourjabar, S., and Choi, G.S. (2021). A high-throughput multi-mode LDPC decoder for 5G NR. arXiv.","DOI":"10.1002\/cta.3208"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1080\/01457632.2015.963444","article-title":"Neuro-fuzzy modeling of the free convection heat transfer from a wavy surface","volume":"36","author":"Karami","year":"2015","journal-title":"Heat Transf. Eng."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1108\/IJPCC-07-2019-0053","article-title":"Tasks mapping in the network on a chip using an improved optimization algorithm","volume":"16","author":"Darbandi","year":"2020","journal-title":"Int. J. Pervasive Comput. Commun."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.measurement.2018.07.026","article-title":"Online measuring density of oil products in annular regime of gas-liquid two phase flows","volume":"129","author":"Roshani","year":"2018","journal-title":"Measurement"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"43","DOI":"10.5937\/jaes16-12829","article-title":"Modeling transportation supply and demand forecasting using artificial intelligence parameters (Bayesian model)","volume":"16","author":"Arabi","year":"2018","journal-title":"J. Appl. Eng. Sci."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.aeue.2018.12.014","article-title":"Design of a very compact and sharp bandpass diplexer with bended lines for GSM and LTE applications","volume":"99","author":"Roshani","year":"2019","journal-title":"AEU Int. J. Electron. Commun."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Juliani, C., and Ellefmo, S.L. (2019). Prospectivity mapping of mineral deposits in Northern Norway using radial basis function neural networks. Minerals, 9.","DOI":"10.3390\/min9020131"},{"key":"ref_64","first-page":"321","article-title":"Multivariable functional interpolation and adaptive networks","volume":"2","author":"Broomhead","year":"1988","journal-title":"Complex Syst."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1162\/neco.1989.1.2.281","article-title":"Fast learning in networks of locally-tuned processing units","volume":"1","author":"Moody","year":"1989","journal-title":"Neural Comput."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/S0893-6080(01)00027-2","article-title":"Three learning phases for radial-basis-function networks","volume":"14","author":"Schwenker","year":"2001","journal-title":"Neural Netw."},{"key":"ref_67","unstructured":"(2012). MATLAB 8.0 and Statistics Toolbox 8.1, The MathWorks, Inc."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/7\/1198\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:25:11Z","timestamp":1760163911000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/7\/1198"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,2]]},"references-count":67,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["sym13071198"],"URL":"https:\/\/doi.org\/10.3390\/sym13071198","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,2]]}}}