Skip to main content
Log in

Artificial neural networks training via bio-inspired optimisation algorithms: modelling industrial winding process, case study

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

This research provides a study on how the weights of artificial neural networks (ANNs) can be automatically updated by applying bio-inspired algorithms, particularly using the particle swarm optimisation (PSO) algorithm, grasshopper optimisation algorithm (GOA) and grey wolf optimisation (GWO). These evolutionary computation algorithms were used to evolve the synaptic weights of ANNs to find a particular architecture of ANNs. The developed nonlinear models were targeted to the identification of a particular nonlinear prediction system, an industrial winding process, as a case study. These new models were referred, respectively, to as ANN-PSO, ANN-GOA and ANN-GWO. The proposed models were compared with other linear and nonlinear conventional models including least square error and multiple nonlinear regression methods, respectively, as well as other state-of-the-art models including multilayer perceptron-type NNs, radial basis function and recurrent local linear neuro-fuzzy. The performance of the developed models was assessed using several metric criteria. Comparison of the proposed ANN-PSO, ANN-GOA and ANN-GWO models with other traditional and state-of-the-art models asserts the efficacy of the proposed modelling approaches.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

Notes

  1. https://homes.esat.kuleuven.be/~smc/daisy/daisydata.html.

References

  • Al-Azzeh J, Alqadi Z, Abuzalata M (2019) Performance analysis of artificial neural networks used for color image recognition and retrieving. Int J Comput Sci Mob Comput 8(2):20–33

    Google Scholar 

  • Ansari A, Gupta NK (2011) Automated diagnosis of coronary heart disease using neuro-fuzzy integrated system. In: World congress on information and communication technologies. IEEE, vol 2011, pp 1379–1384

  • Arunkumar N, Mohammed MA, Mostafa SA, Ibrahim DA, Rodrigues JJ, de Albuquerque VHC (2020) Fully automatic model-based segmentation and classification approach for MRI brain tumor using artificial neural networks. Concurr Comput Pract Exp 32(1):e4962

    Google Scholar 

  • Asadi Asad Abad MR, Borghei AM, Ahmadi H, Minaei S, Beheshtivol B (2015) Fuzzy logic based classification of faults in mechanical differential. J Vibroeng 17(7):3635–3649

    Google Scholar 

  • Ayough A, Khorshidvand B (2019) Designing a manufacturing cell system by assigning workforce. J Ind Eng Manag 12(1):13–26

    Google Scholar 

  • Azizivahed A, Narimani H, Fathi M, Naderi E, Safarpour HR, Narimani MR (2018) Multi-objective dynamic distribution feeder reconfiguration in automated distribution systems. Energy 147:896–914

    Google Scholar 

  • Babuška R, Verbruggen H (2003) Neuro-fuzzy methods for nonlinear system identification. Annu Rev Control 27(1):73–85

    Google Scholar 

  • Bastogne T, Noura H, Sibille P, Richard A (1998) Multivariable identification of a winding process by subspace methods for tension control. Control Eng Pract 6(9):1077–1088

    Google Scholar 

  • Braatz RD, Ogunnaike BA, Featherstone AP (1996) Identification, estimation, and control of sheet and film processes. IFAC Proc Vol 29(1):6638–6643

    Google Scholar 

  • Braik M, Sheta A, Arieqat A (2008) A comparison between GAs and PSO in training ANN to model the TE chemical process reactor. In: Proceedings of the AISB 2008 convention in communication, interaction and social intelligence, vol 1 , p 24

  • Chang Y-W, Hsieh C-J, Chang K-W, Ringgaard M, Lin C-J (2010) Training and testing low-degree polynomial data mappings via linear SVM. J Mach Learn Res 11(Apr):1471–1490

    MathSciNet  MATH  Google Scholar 

  • Chang P-C, Wu J-L, Xu Y, Zhang M, Lu X-Y (2019) Bike sharing demand prediction using artificial immune system and artificial neural network. Soft Comput 23(2):613–626

    Google Scholar 

  • Crone SF, Kourentzes N (2009) Input-variable specification for neural networks-an analysis of forecasting low and high time series frequency. In: International joint conference on neural networks, IJCNN 2009. IEEE, pp 619–626

  • Dao SD, Abhary K, Marian R (2017) Optimisation of assembly scheduling in VCIM systems using genetic algorithm. J Ind Eng Int 13(3):275–296

    Google Scholar 

  • Dixit SR, Das SR, Dhupal D (2019) Parametric optimization of Nd: Yag laser microgrooving on aluminum oxide using integrated RSM-ANN-GA approach. J Ind Eng Int 15(2):333–349

    Google Scholar 

  • El-Thalji I, Jantunen E (2015) A summary of fault modelling and predictive health monitoring of rolling element bearings. Mech Syst Signal Process 60:252–272

    Google Scholar 

  • Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32(200):675–701

    MATH  Google Scholar 

  • Gupta S, Deep K (2018) A novel random walk grey wolf optimizer. Swarm Evol Comput 44:101–112

    Google Scholar 

  • Haddad M, Guillaumat L, Terekhina S, Crozatier M (2017) Analytical and numerical study based on experimental investigation of different curved sandwich composites manufactured by filament winding process. J Compos Mater 2017:0021998317714858

    Google Scholar 

  • Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6:65–70

    MathSciNet  MATH  Google Scholar 

  • Hussein E, Sheta A, El Wahab A (2001) Modeling of a winding machine using non-parametric neural networks. In: WSEAS international conference on scientific computation and soft computing, pp 528–533

  • Hussian A, Sheta A, Kamel M, Telbaney M, Abdelwahab A (2000) Modeling of a winding machine using genetic programming. In: Proceedings of the 2000 congress on evolutionary computation, IEEE, vol 1, pp 398–402

  • Jain M, Narayan S, Balaji P, Bhowmick A, Muthu RK et al (2020) Speech emotion recognition using support vector machine. arXiv:2002.07590

  • Kennedy J (2011) Particle swarm optimization. In: Encyclopedia of machine learning. Springer, pp 760–766

  • Khosravi A, Koury R, Machado L, Pabon J (2018) Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system. Sustain Energy Technol Assess 25:146–160

    Google Scholar 

  • Li J, Cheng J-H, Shi J-Y, Huang F (2012) Brief introduction of back propagation (BP) neural network algorithm and its improvement. In: Advances in computer science and information engineering. Springer, pp 553–558

  • Liu J, Shao Y (2017) Dynamic modeling for rigid rotor bearing systems with a localized defect considering additional deformations at the sharp edges. J Sound Vib 398:84–102

    Google Scholar 

  • Liu J, Shao Y (2018) Overview of dynamic modelling and analysis of rolling element bearings with localized and distributed faults. Nonlinear Dyn 93:1765–1798

    Google Scholar 

  • Liu J, Shao Y, Lim TC (2014) Impulse vibration transmissibility characteristics in the presence of localized surface defects in deep groove ball bearing systems. Proc Inst Mech Eng Part K J Multi-body Dyn 228(1):62–81

    Google Scholar 

  • Ljung L (1987) Theory for the user. Prentice Hall, Upper Saddle River

    MATH  Google Scholar 

  • Masadeh R, Alzaqebah A, Hudaib A, Rahman AA (2018) Grey wolf algorithm for requirements prioritization. Mod Appl Sci 12(2):54

    Google Scholar 

  • Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Google Scholar 

  • Moslemipour G (2018) A hybrid CS-SA intelligent approach to solve uncertain dynamic facility layout problems considering dependency of demands. J Ind Eng Int 14(2):429–442

    Google Scholar 

  • Mousavi SH, Nazemi A, Hafezalkotob A (2015) Using and comparing metaheuristic algorithms for optimizing bidding strategy viewpoint of profit maximization of generators. J Ind Eng Int 11(1):59–72

    Google Scholar 

  • Naderpour H, Mirrashid M (2019) Shear failure capacity prediction of concrete beam–column joints in terms of ANFIS and GMDH. Pract Period Struct Des Constr 24(2):04019006

    Google Scholar 

  • Nikabadi M, Naderi R (2016) A hybrid algorithm for unrelated parallel machines scheduling. Int J Ind Eng Comput 7(4):681–702

    Google Scholar 

  • Noura H, Theilliol D, Ponsart J-C, Chamseddine A (2009) Fault-tolerant control systems: design and practical applications. Springer, Berlin

    MATH  Google Scholar 

  • Nozari HA, Banadaki HD, Mokhtare M, Vahed SH (2012) Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks. J Zhejiang Univ Sci C 13(6):403–412

    Google Scholar 

  • Omotosho A, Oluwatobi AE, Oluwaseun OR, Chukwuka AE, Adekanmi A (2018) A neuro-fuzzy based system for the classification of cells as cancerous or non-cancerous. Int J Med Res Health Sci 7(5):155–166

    Google Scholar 

  • Parant F, Coeffier C, Iung C (1992) Modeling of web tension in a continuous annealing line. Iron Steel Eng (USA) 69(11):46–49

    Google Scholar 

  • Pirdashti M, Curteanu S, Kamangar MH, Hassim MH, Khatami MA (2013) Artificial neural networks: applications in chemical engineering. Rev Chem Eng 29(4):205–239

    Google Scholar 

  • Rajasekaran MP, Sri Meena R (2012) Application of adaptive neuro-fuzzy inference systems for MR image classification and tumour detection. Int J Biomed Eng Technol 9(2):133–146

    Google Scholar 

  • Ranganathan A (2004) The Levenberg–Marquardt algorithm. Tutor LM Algorithm 11(1):101–110

    Google Scholar 

  • Sadati N, Chinnam RB, Nezhad MZ (2018) Observational data-driven modeling and optimization of manufacturing processes. Expert Syst Appl 93:456–464

    Google Scholar 

  • Santillan JH, Tapucar S, Manliguez C, Calag V (2018) Cuckoo search via Lévy flights for the capacitated vehicle routing problem. J Ind Eng Int 14(2):293–304

    Google Scholar 

  • Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47

    Google Scholar 

  • Saritas MM, Yasar A (2019) Performance analysis of ANN and Naive Bayes classification algorithm for data classification. Int J Intell Syst Appl Eng 7(2):88–91

    Google Scholar 

  • Schlei-Peters I, Wichmann MG, Matthes I-G, Gundlach F-W, Spengler TS (2018) Integrated material flow analysis and process modeling to increase energy and water efficiency of industrial cooling water systems. J Ind Ecol 22(1):41–54

    Google Scholar 

  • Sheta AF, Braik M, Al-Hiary H (2009) Identification and model predictive controller design of the Tennessee Eastman Chemical Process using ANN. In: Proceedings of the international conference on artificial intelligence (ICAI’09), July 13–16, USA, vol 1, pp 25–31

  • Sheta AF, Braik M, Öznergiz E, Ayesh A, Masud, M (2013) Design and automation for manufacturing processes: an intelligent business modeling using adaptive neuro-fuzzy inference systems. In: Business intelligence and performance management. Springer, pp 191–208

  • Sheta AF, Öznergiz E, Abdelrahman M, Babuska R (2009) Modeling of hot rolling industrial process using fuzzy logic. In: CAINE, pp 81–86

  • Sheta A, Braik M, Al-Hiary H (2019) Modeling the Tennessee Eastman chemical process reactor using bio-inspired feedforward neural network (bi-ff-nn). Int J Adv Manuf Technol 103(1–4):1359–1380

    Google Scholar 

  • Sievers L, Balas MJ, von Flotow A (1988) Modeling of web conveyance systems for multivariable control. IEEE Trans Autom Control 33(6):524–531

    MATH  Google Scholar 

  • Torres PJR, Mercado ES, Rifón LA (2018) Probabilistic Boolean network modeling of an industrial machine. J Intell Manuf 29(4):875–890

    Google Scholar 

  • Wang Y-R, Yu C-Y, Chan H-H (2012) Predicting construction cost and schedule success using artificial neural networks ensemble and support vector machines classification models. Int J Project Manag 30(4):470–478

    Google Scholar 

  • Wang G, Tang W, Xia J, Chu J, Noorman H, Gulik WM (2015) Integration of microbial kinetics and fluid dynamics toward model-driven scale-up of industrial bioprocesses. Eng Life Sci 15(1):20–29

    Google Scholar 

  • Wang Y, Li H, Qi C (2020) An adaptive mode convolutional neural network based on bar-shaped structures and its operation modeling to complex industrial processes. Chemom Intell Lab Syst 2020:103932

    Google Scholar 

  • Yıldız AR (2008) Hybrid Taguchi-Harmony search algorithm for solving engineering optimization problems. Int J Ind Eng 15(3):286–293

    Google Scholar 

  • Zhang X, Han Q, Peng Z, Chu F (2016) A comprehensive dynamic model to investigate the stability problems of the rotor-bearing system due to multiple excitations. Mech Syst Signal Process 70:1171–1192

    Google Scholar 

  • Zingg DW, Nemec M, Pulliam TH (2008) A comparative evaluation of genetic and gradient-based algorithms applied to aerodynamic optimization. Eur J Comput Mech 17(1–2):103–126

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hussein Al-Zoubi.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Human and animal rights statememt

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (rar 1387 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Braik, M., Al-Zoubi, H. & Al-Hiary, H. Artificial neural networks training via bio-inspired optimisation algorithms: modelling industrial winding process, case study. Soft Comput 25, 4545–4569 (2021). https://doi.org/10.1007/s00500-020-05464-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-020-05464-9

Keywords

Navigation