Artificial neural networks training via bio-inspired optimisation algorithms: modelling industrial winding process, case study
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 (G...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Braik, Malik [verfasserIn] Al-Zoubi, Hussein [verfasserIn] Al-Hiary, Heba [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 25(2020), 6 vom: 23. Nov., Seite 4545-4569 |
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| Übergeordnetes Werk: |
volume:25 ; year:2020 ; number:6 ; day:23 ; month:11 ; pages:4545-4569 |
| Links: |
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| DOI / URN: |
10.1007/s00500-020-05464-9 |
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SPR043428231 |
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| 520 | |a 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. | ||
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| 650 | 4 | |a Industrial winding process |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Multiple nonlinear regression |7 (dpeaa)DE-He213 | |
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10.1007/s00500-020-05464-9 doi (DE-627)SPR043428231 (DE-599)SPRs00500-020-05464-9-e (SPR)s00500-020-05464-9-e DE-627 ger DE-627 rakwb eng Braik, Malik verfasserin aut Artificial neural networks training via bio-inspired optimisation algorithms: modelling industrial winding process, case study 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Artificial neural network (dpeaa)DE-He213 Particle swarm optimisation (dpeaa)DE-He213 Grasshopper optimisation algorithm (dpeaa)DE-He213 Grey wolf optimisation (dpeaa)DE-He213 Industrial winding process (dpeaa)DE-He213 Multiple nonlinear regression (dpeaa)DE-He213 Al-Zoubi, Hussein verfasserin aut Al-Hiary, Heba verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 25(2020), 6 vom: 23. Nov., Seite 4545-4569 (DE-627)SPR006469531 nnns volume:25 year:2020 number:6 day:23 month:11 pages:4545-4569 https://dx.doi.org/10.1007/s00500-020-05464-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 25 2020 6 23 11 4545-4569 |
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10.1007/s00500-020-05464-9 doi (DE-627)SPR043428231 (DE-599)SPRs00500-020-05464-9-e (SPR)s00500-020-05464-9-e DE-627 ger DE-627 rakwb eng Braik, Malik verfasserin aut Artificial neural networks training via bio-inspired optimisation algorithms: modelling industrial winding process, case study 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Artificial neural network (dpeaa)DE-He213 Particle swarm optimisation (dpeaa)DE-He213 Grasshopper optimisation algorithm (dpeaa)DE-He213 Grey wolf optimisation (dpeaa)DE-He213 Industrial winding process (dpeaa)DE-He213 Multiple nonlinear regression (dpeaa)DE-He213 Al-Zoubi, Hussein verfasserin aut Al-Hiary, Heba verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 25(2020), 6 vom: 23. Nov., Seite 4545-4569 (DE-627)SPR006469531 nnns volume:25 year:2020 number:6 day:23 month:11 pages:4545-4569 https://dx.doi.org/10.1007/s00500-020-05464-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 25 2020 6 23 11 4545-4569 |
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10.1007/s00500-020-05464-9 doi (DE-627)SPR043428231 (DE-599)SPRs00500-020-05464-9-e (SPR)s00500-020-05464-9-e DE-627 ger DE-627 rakwb eng Braik, Malik verfasserin aut Artificial neural networks training via bio-inspired optimisation algorithms: modelling industrial winding process, case study 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Artificial neural network (dpeaa)DE-He213 Particle swarm optimisation (dpeaa)DE-He213 Grasshopper optimisation algorithm (dpeaa)DE-He213 Grey wolf optimisation (dpeaa)DE-He213 Industrial winding process (dpeaa)DE-He213 Multiple nonlinear regression (dpeaa)DE-He213 Al-Zoubi, Hussein verfasserin aut Al-Hiary, Heba verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 25(2020), 6 vom: 23. Nov., Seite 4545-4569 (DE-627)SPR006469531 nnns volume:25 year:2020 number:6 day:23 month:11 pages:4545-4569 https://dx.doi.org/10.1007/s00500-020-05464-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 25 2020 6 23 11 4545-4569 |
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10.1007/s00500-020-05464-9 doi (DE-627)SPR043428231 (DE-599)SPRs00500-020-05464-9-e (SPR)s00500-020-05464-9-e DE-627 ger DE-627 rakwb eng Braik, Malik verfasserin aut Artificial neural networks training via bio-inspired optimisation algorithms: modelling industrial winding process, case study 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Artificial neural network (dpeaa)DE-He213 Particle swarm optimisation (dpeaa)DE-He213 Grasshopper optimisation algorithm (dpeaa)DE-He213 Grey wolf optimisation (dpeaa)DE-He213 Industrial winding process (dpeaa)DE-He213 Multiple nonlinear regression (dpeaa)DE-He213 Al-Zoubi, Hussein verfasserin aut Al-Hiary, Heba verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 25(2020), 6 vom: 23. Nov., Seite 4545-4569 (DE-627)SPR006469531 nnns volume:25 year:2020 number:6 day:23 month:11 pages:4545-4569 https://dx.doi.org/10.1007/s00500-020-05464-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 25 2020 6 23 11 4545-4569 |
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Artificial neural networks training via bio-inspired optimisation algorithms: modelling industrial winding process, case study |
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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. |
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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. |
| abstract_unstemmed |
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. |
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Artificial neural networks training via bio-inspired optimisation algorithms: modelling industrial winding process, case study |
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https://dx.doi.org/10.1007/s00500-020-05464-9 |
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Al-Zoubi, Hussein Al-Hiary, Heba |
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Al-Zoubi, Hussein Al-Hiary, Heba |
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10.1007/s00500-020-05464-9 |
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2024-07-03T18:36:01.204Z |
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