Top

Artificial Intelligence Review

Published in:

18-04-2020

Intelligent modelling to predict heat transfer coefficient of vacuum glass insulation based on thinking evolutionary neural network

Authors: Wang Lei, Omary Gastro, Yuanqi Wang, Nomenjanahary Homary Felicien, Li Hui

Published in: Artificial Intelligence Review | Issue 8/2020

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Vacuum glass is widely used in many construction applications, including single-family homes, as a proven energy-saving method with outstanding heat preservation characteristics. The thermal insulation performance of vacuum glass is closely related to its heat transfer coefficient. In this study, we applied neural network methods to predict the heat transfer coefficients of vacuum glass. Using MATLAB, a neural network intelligence model was established, and the traditional back-propagation neural network (BPNN) was optimised. First, a genetic algorithm was used to reduce the dimensions of the independent variable. Then, the Mind Evolutionary Computation algorithm was used to optimise the initial weight and threshold. Using the optimised BPNN intelligence model to predict the heat transfer coefficient of vacuum glass insulation, we derived an average absolute error of 0.0076.

previous article Selective ensemble of uncertain extreme learning machine for pattern classification with missing features

next article A review on the long short-term memory model

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Amari S-I (1998) Natural gradient works efficiently in learning. Neural Comput 10:251–276

Benardos P, Vosniakos G-C (2007) Optimizing feedforward artificial neural network architecture. Eng Appl Artif Intell 20:365–382

Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, New YorkMATH

Chang H (2010) Research on underwater target recognition method based on BP neural network. Northeastern University, Shen yang

Chengyi C, Sun Y, Wei L (1998) Mind-evolution-based machine learning: framework and the implementation of optimization. In: Proceedings of IEEE intelligent conference on intelligent engineering systems, pp 355–359

Collins RE, Simko TM (1998) Current status of the science and technology of vacuum glazing. Sol Energy 62:189–213

Dehghan M, Hajarian M (2009) A lower bound for the product of eigenvalues of solutions to matrix equations. Appl Math Lett 22:1786–1788MathSciNetMATH

Dehghan M, Hajarian M (2010) An iterative method for solving the generalized coupled Sylvester matrix equations over generalized bisymmetric matrices. Appl Math Model 34:639–654MathSciNetMATH

Dehghan M, Hajarian M (2011) Analysis of an iterative algorithm to solve the generalized coupled Sylvester matrix equations. Appl Math Model 35:3285–3300MathSciNetMATH

Ding F (2013a) Hierarchical multi-innovation stochastic gradient algorithm for Hammerstein nonlinear system modeling. Appl Math Model 37:1694–1704MathSciNetMATH

Ding F (2013b) Two-stage least squares based iterative estimation algorithm for CARARMA system modeling. Appl Math Model 37:4798–4808MathSciNetMATH

Ding F, Liu YJ, Bao B (2012) Gradient based and least squares based iterative estimation algorithms for multi-input multi-output systems. Proc Inst Mech Eng Part I J Syst Control Eng 226:43–55

Du P, Wang L, Wang Y-Q (2016) Research on forecasting vacuum glazing demand based on grey neural network with MEC algorithm. Vacuum 5:25–28 (in Chinese)

EN ISO 10077-1 (2000) Thermal Performance of Windows, Doors and shutters – Calculation of Thermal Transmittance – Part 1: Simplified Method. European Committee for Standardization CEN, Brussels

Fuller M, Ritter G, Draper C (1988) Partial least squares quantitative analysis of infrared spectroscopic data. Part I: Algorithm implementation. Appl Spectrosc 42:217–227

Graves R, Zarr R (1997) Insulation material: testing and applications, 3rd edn. ASTM-STP 1320

Gross J, Fricke J (1997) Non-destructive In-situ determination of the rare gas content of highly insulating glazing units. In: Graves RS, Zarr RR (eds) Insulating materials: testing and Applications, 3rd edn. ASTM STP 1320

Haykin S (1999) Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice Hall PTR, Upper Saddle RiverMATH

Holland C (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

Hou Y-B, Wang M, Wang L-Q (2004) System Identification and the MATLAB Simulation. Science Press, Beijing

Houck CR, Jones JA, Kay MG (1996) A genetic algorithm for function optimization: a MATLAB implementation. NCSU-IE Technical Report 95-09

Hunt KJ, Sbarbaro D, Zbikowski R, Gawthrop PJ (1992) Neural networks for control systems—a survey. Automatica 28:1083–1112MathSciNetMATH

Kemsley EK (1998) A genetic algorithm (GA) approach to the calculation of canonical variates. Trends Anal Chem 17:24–34

Krummenauer R, Cazarotto M, Lopes A, Larzabal P, Forster P (2010) Improving the threshold performance of maximum likelihood estimation of direction of arrival. Sig Process 90:1582–1590MATH

Lacrama DL, Viscu LI, Drugarin CVA (2016) Artificial vs. natural neural networks. In: 2016 13th symposium on neural networks and applications (NEUREL)

Lei W, Gastro O, Wang Y, HomaryFelicien N (2019) Computational fluid-dynamics-based simulation of heat transfer through vacuum glass. Eur Phys J Plus 134(7):2019

Lin X (2009) Research on the growth evaluation of listed retail enterprises based on BP neural network. Huaqiao University (in Chinese)

Liu YJ, Yu L, Ding F (2010) Multi-innovation extended stochastic gradient algorithm and its performance analysis. Circuits Syst Signal Process 29:649–667MathSciNetMATH

Liu D, Archer N, Duesing K, Hannan G, Keast R (2016) Mechanism of fat taste perception: association with diet and obesity. Prog Lipid Res 63:41–49

Lucasius C, Kateman G (1993) Understanding and using genetic algorithms. Part 1: concepts, properties and context. Chemometr Intell Lab Syst 19:1–33

Lucasius CB, Beckers MLM, Kateman G (1994) Genetic algorithms in wavelength selection: a comparative study. Anal Chim Acta 286:135–153

Malhotra K, Chilkuri K, Ranka S (1997) Elements of Artificial Neural Networks, 2nd edn. Penram International Publishing, Mumbai

Manz H, Brunner S, Wullschleger L (2006) TVG: heat transfer and basic mechanical constraints. Sol Energy 80:1632–1642

McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133MathSciNetMATH

Piuri V, Alippi C (1998) Artificial neural networks. J Syst Architect 44:565–567

Rizogiannis C, Kofidis E, Papadias CB, Theodoridis S (2010) Semi-blind maximum-likelihood joint channel/data estimation for correlated channels in multiuser MIMO networks. Sig Process 90:1209–1224MATH

Suraweera NP, Ranasinghe DNA (2008) Natural algorithmic approach to the structural optimisation of neural networks. In: 4th International conference on information and automation for sustainability, 2008, pp 150–156

Twickel A, Büschges A, Pasemann F (2011) Deriving neural network controllers from neuro-biological data: implementation of a single-leg stick insect controller. Biol Cybern 104:95–119

Wang H, Wei X, Chen J et al (2007) Analysis of artificial neural networks. Sci China Inf Sci 18:319–320

Wang DQ, Ding F, Chu YY (2013a) Data filtering based recursive least squares algorithm for Hammerstein systems using the key-term separation principle. Inf Sci 222:203–212MathSciNetMATH

Wang X-C, Shi F, Yu L (2013b) 43 cases analysis of MATLAB neural network. Beihang University Press, Beijing

White H (1988) Economic prediction using neural networks: the case of IBM daily stock returns. In: IEEE international conference on neural networks, vol 452, pp 451–458

Xie Y, Liu L, Li G et al (2015) Research on the acquisition of circuit board fault signs based on BP neural network. J Sichuan Ordnance Ind 10:95–98 (in Chinese)

Xing L, Pham D (1995) Neural networks for identification, prediction, and control. Springer, New York

Yin HH, Zhu ZF, Ding F (2011) Model order determination using the Hankel matrix of impulse responses. Appl Math Lett 24:797–802MathSciNetMATH

You B, Li Z, Huang L (2018) Hand-motion recognition based on improved BP neural network. CAAI Trans Intell Syst 13(5):848–854 (in Chinese)

Zadeh L (1956) On the identification problem. IRE Trans Circuit Theory 3:277–281

Zayegh A, Al Bassam N (2018) Neural network principles and applications. In: Vahid Asadpour (ed) Digital systems, IntechOpen

Zeng L (2009) Direct inversion of ocean color remote sensing algorithm. PhD Thesis, Huazhong University of Science and Technology, June 2009 (in Chinese)

Zhang S (2011) Risk management model based on neural network algorithm. Shantou University (in Chinese)

Zhang Y (2017) Research on spectral region selection of near infrared spectra based on genetic algorithm. In: 9th International conference on intelligent human-machine systems and cybernetics (IHMSC), 2017

Zhang Y, Cui GM (2011) Bias compensation methods for stochastic systems with colored noise. Appl Math Model 35:1709–1716MathSciNetMATH

Zhao Z, Zhang Y, Hu J, Li L (2009) Comparative study of PCA and ICA based traffic flow compression. J Highw Transp Res Dev (Eng Ed) 4:98–102

Zhou K, Kang Y (2005) Neural network model and its MATLAB simulation program design. Tsinghua University Publishing House, Beijing, p 7

Zhou Z, Ding T, Tian Y et al (2006) The study and application of genetic algorithm optimization toolbox in MATLAB. Mech Res Appl 19(6):69–71 (in Chinese)

Zhu K, Wang Z (2010) Proficient in MATLAB neural network. Publishing House of Electronics Industry, Beijing

Zhu S, Wang Y, Zhang X et al (2004) Region selection method of near infrared spectrum based on genetic algorithm (in Chinese). J Agric Mach 35(5):152–156

Title: Intelligent modelling to predict heat transfer coefficient of vacuum glass insulation based on thinking evolutionary neural network
Authors: Wang Lei
Omary Gastro
Yuanqi Wang
Nomenjanahary Homary Felicien
Li Hui
Publication date: 18-04-2020
Publisher: Springer Netherlands
Published in: Artificial Intelligence Review / Issue 8/2020
Print ISSN: 0269-2821
Electronic ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-020-09837-2

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Other articles of this Issue 8/2020

Image segmentation evaluation: a survey of methods

Empirical evaluation and study of text stemming algorithms

Novel classes of coverings based multigranulation fuzzy rough sets and corresponding applications to multiple attribute group decision-making

CHIRPS: Explaining random forest classification

Selective ensemble of uncertain extreme learning machine for pattern classification with missing features

Design pattern detection approaches: a systematic review of the literature

Premium Partner