Application of a Narrow Neural Network Algorithm to Dynamic Analysis Results of a One-Link Planar Robotic Arm
Öz
The kinematics, dynamics, control, and optimal design of robotic mechanisms have been widely investigated using various analytical and computational approaches. In recent years, artificial neural network (ANN) algorithms have emerged as an additional tool in mechanism and robotics research. Experimental and theoretical results are commonly analyzed using supervised and unsupervised neural network algorithms. However, many existing studies do not explicitly examine the effects of input–output variations within the employed datasets. In this study, the dynamic modeling and simulation of a one-link planar robotic arm are first presented. Based on the simulation results, datasets are generated and analyzed using a neural network algorithm. One of the primary objectives of this study is to demonstrate the importance of appropriate input–output (predictor–response) selection in neural network applications for robotic mechanisms. A further objective is to contribute ongoing scientific investigations about robotic modeling which use neural network algorithms. The results show that different choices of input and output variables can lead to significantly different prediction performances, highlighting the critical role of dataset formulation in neural network–based robotic analysis.
Anahtar Kelimeler
ANN dynamic analysis narrow neural network one-link mechanism robotic arm
Etik Beyan
N/A
Destekleyen Kurum
N/A
Proje Numarası
N/A
Teşekkür
N/A
Kaynakça
- [1] Spong, M. W., Hutchinson, S., & Vidyasagar, M. (2020). Robot modeling and control (2nd ed.). Wiley.
- [2] Söylemez, E. (2018). Makina teorisi 2: Makine dinamiği. Birsen Yayınevi.
- [3] Waldron, K. J., Kinzel, G. L., & Agrawal, S. K. (2016). Kinematics, dynamics, and design of machinery (3rd ed.). John Wiley & Sons.
- [4] Abduljabbar, Z., ElMadany, M. M., & Al-Dokhiel, H. D. (1993). Controller design of a one-link flexible robot arm. Computers & Structures, 49(1), 117–126. https://doi.org/10.1016/0045-7949(93)90130-6
- [5] Raju, E. M., Krishna, L. S., Mouli, Y. S., & Rao, V. N. (2015). Effect of link flexibility on tip position of a single link robotic arm. Journal of Physics: Conference Series, 662(1), 012020. https://doi.org/10.1088/1742-6596/662/1/012020
- [6] Yang, G. B., & Donath, M. (1988, April). Dynamic model of a one-link robot manipulator with both structural and joint flexibility. In Proceedings of the IEEE International Conference on Robotics and Automation (pp. 476–481). IEEE. https://doi.org/10.1109/ROBOT.1988.12097
- [7] De Luca, A., & Siciliano, B. (1989). Trajectory control of a nonlinear one-link flexible arm. International Journal of Control, 50(2), 699–715. https://doi.org/10.1080/00207178908953460
- [8] Endo, T., Matsuno, F., & Kawasaki, H. (2014). Force control and exponential stabilisation of one-link flexible arm. International Journal of Control, 87(9), 794–807. https://doi.org/10.3182/20050703-6-CZ-1902.01313
- [9] Shchuka, A., & Goldenberg, A. A. (1989). Tip control of a single-link flexible arm using a feedforward technique. Mechanism and Machine Theory, 24(5), 439–455. https://doi.org/10.1016/0094-114X(89)90072-4
- [10] M.E. Demir, and R.K. Ergün, "Mechanical and wear performance of Al-, mica-, SiO2-filled glass fiber-reinforced composites and prediction of wear properties with artificial neural networks," In Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, p. 09544089241307840, 2 Jan. 2025, doi: 10.1177/095440892413078
- [11] M.E. Demir, and R.K. Ergün, "Mechanical and wear performance of Al-, mica-, SiO2-filled glass fiber-reinforced composites and prediction of wear properties with artificial neural networks," In Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, p. 09544089241307840, 2 Jan. 2025, doi: 10.1177/095440892413078.
- [12] Demir, M. E., Çetkin, E., Ergün, R. K., & Denizhan, O. (2024). Tribological and mechanical properties of nanofilled glass fiber reinforced composites and analyzing the tribological behavior using artificial neural networks. Polymer Composites, 45(5), 4233–4249. https://doi.org/10.1002/pc.28055
- [13] Demir, M. E. (2025). The effect of filler type (tungsten carbide, zinc oxide) and content on the mechanical and wear behavior of jute/flax reinforced epoxy hybrid composites: Experimental and artificial neural network analysis. Polymer Composites. https://doi.org/10.1002/pc.29879
- [14] Gürbüz, H., Demir, M. E., Baday, Ş., & Akcan, İ. H. (2025). Yapay sinir ağı ile mikro parçacık dolgulu kompozitlerin tribolojik özelliklerinin tahmini. Journal of Materials and Mechatronics: A, 6(1), 68–82. https://doi.org/10.55546/jmm.1581662
- [15] Denizhan, O. (2025). Application of various artificial neural network algorithms for regression analysis in the dynamic modeling of a three-link planar RPR robotic arm. Machines, 13(11), 1031. https://doi.org/10.3390/machines13111031
- [16] Denizhan, O. (2024). Comparison of different supervised learning algorithms for position analysis of the slider-crank mechanism. Alexandria Engineering Journal, 92, 39–49. https://doi.org/10.1016/j.aej.2024.02.055
- [17] Kong, F. G., Li, Q., & Zhang, W. J. (1999). An artificial neural network approach to mechanism kinematic chain isomorphism identification. Mechanism and Machine Theory, 34(2), 271–283. https://doi.org/10.1016/S0094-114X(98)00035-4
- [18] Efe, M. O., & Kaynak, O. (2000). Stabilizing and robustifying the learning mechanisms of artificial neural networks in control engineering applications. International Journal of Intelligent Systems, 15(5), 365–388. https://doi.org/10.1002/(SICI)1098-111X(200005)15:5
- [19] Yang, Z., Wu, J., & Mei, J. (2007). Motor-mechanism dynamic model based neural network optimized computed torque control of a high speed parallel manipulator. Mechatronics, 17(7), 381–390. https://doi.org/10.1016/j.mechatronics.2007.04.009
- [20] Cui, H. (2025). High-dimensional learning of narrow neural networks. Journal of Statistical Mechanics: Theory and Experiment, 2025(2), 023402. https://doi.org/10.1088/1742-5468/adb1d6
- [21] Beise, H. P., Da Cruz, S. D., & Schröder, U. (2021). On decision regions of narrow deep neural networks. Neural Networks, 140, 121–129. https://doi.org/10.1016/j.neunet.2021.02.024
- [22] Lu, L., Su, Y., & Karniadakis, G. E. (2018). Collapse of deep and narrow neural nets. arXiv preprint arXiv:1808.04947. https://doi.org/10.48550/arXiv.1808.04947
- [23] Babu, M. V., & Banana, K. (2024). A study on narrow artificial intelligence—An overview. International Journal of Engineering Science and Advanced Technology, 24(4), 210–219
- [24] Alali, M., Sharef, N. M., Murad, M. A., Hamdan, H., & Husin, N. A. (2019). Narrow convolutional neural network for Arabic dialects polarity classification. IEEE Access, 7, 96272–96283. https://doi.org/10.1109/ACCESS.2019.2929208
- [25] Lee, H., Kim, Y., Yang, S. Y., & Choi, H. (2024). Improved weight initialization for deep and narrow feedforward neural network. Neural Networks, 176, 106362. https://doi.org/10.1016/j.neunet.2024.106362
- [26] Dommel, J., & Wegner, S. A. (2025). An in-depth look at approximation via deep and narrow neural networks. Neurocomputing. https://doi.org/10.1016/j.neucom.2025.131769
Dar Yapılı Bir Yapay Sinir Ağı Algoritmasının Tek Bağlantılı Düzlemsel Bir Robot Kolunun Dinamik Analiz Sonuçlarına Uygulanması
Öz
Robotik mekanizmaların kinematiği, dinamiği, kontrolü ve optimal tasarımı çeşitli analitik ve hesaplamalı yaklaşımlar kullanılarak geniş ölçüde incelenmiştir. Son yıllarda, yapay sinir ağı (YSA) algoritmaları mekanizma ve robotik araştırmalarında ek bir araç olarak ortaya çıkmıştır. Deneysel ve teorik sonuçlar yaygın olarak denetimli ve denetimsiz sinir ağı algoritmaları kullanılarak analiz edilmektedir. Ancak, mevcut çalışmaların birçoğu kullanılan veri kümeleri içerisindeki girdi–çıktı değişimlerinin etkilerini açıkça incelememektedir. Bu çalışmada, tek bağlantılı (one-link) düzlemsel bir robot kolunun dinamik modellemesi ve simülasyonu öncelikle sunulmaktadır. Simülasyon sonuçlarına dayanarak veri kümeleri oluşturulmuş ve bir sinir ağı algoritması kullanılarak analiz edilmiştir. Bu çalışmanın temel amaçlarından biri, robotik mekanizmalarda sinir ağı uygulamalarında uygun girdi–çıktı (tahmin edici–yanıt) seçiminin önemini göstermektir. Bir diğer amaç ise sinir ağı algoritmalarını kullanan robotik modelleme konusundaki devam eden bilimsel çalışmalara katkıda bulunmaktır. Sonuçlar, farklı girdi ve çıktı değişkeni seçimlerinin önemli ölçüde farklı tahmin performanslarına yol açabileceğini göstermekte ve sinir ağı tabanlı robotik analizde veri kümesi oluşturmanın kritik rolünü vurgulamaktadır.
Anahtar Kelimeler
YSA dinamik analiz Dar Yapılı Yapay Sinir Ağı Tek Bağlantılı mekanizma robotik kol
Etik Beyan
N/A
Destekleyen Kurum
N/A
Proje Numarası
N/A
Teşekkür
N/N
Kaynakça
- [1] Spong, M. W., Hutchinson, S., & Vidyasagar, M. (2020). Robot modeling and control (2nd ed.). Wiley.
- [2] Söylemez, E. (2018). Makina teorisi 2: Makine dinamiği. Birsen Yayınevi.
- [3] Waldron, K. J., Kinzel, G. L., & Agrawal, S. K. (2016). Kinematics, dynamics, and design of machinery (3rd ed.). John Wiley & Sons.
- [4] Abduljabbar, Z., ElMadany, M. M., & Al-Dokhiel, H. D. (1993). Controller design of a one-link flexible robot arm. Computers & Structures, 49(1), 117–126. https://doi.org/10.1016/0045-7949(93)90130-6
- [5] Raju, E. M., Krishna, L. S., Mouli, Y. S., & Rao, V. N. (2015). Effect of link flexibility on tip position of a single link robotic arm. Journal of Physics: Conference Series, 662(1), 012020. https://doi.org/10.1088/1742-6596/662/1/012020
- [6] Yang, G. B., & Donath, M. (1988, April). Dynamic model of a one-link robot manipulator with both structural and joint flexibility. In Proceedings of the IEEE International Conference on Robotics and Automation (pp. 476–481). IEEE. https://doi.org/10.1109/ROBOT.1988.12097
- [7] De Luca, A., & Siciliano, B. (1989). Trajectory control of a nonlinear one-link flexible arm. International Journal of Control, 50(2), 699–715. https://doi.org/10.1080/00207178908953460
- [8] Endo, T., Matsuno, F., & Kawasaki, H. (2014). Force control and exponential stabilisation of one-link flexible arm. International Journal of Control, 87(9), 794–807. https://doi.org/10.3182/20050703-6-CZ-1902.01313
- [9] Shchuka, A., & Goldenberg, A. A. (1989). Tip control of a single-link flexible arm using a feedforward technique. Mechanism and Machine Theory, 24(5), 439–455. https://doi.org/10.1016/0094-114X(89)90072-4
- [10] M.E. Demir, and R.K. Ergün, "Mechanical and wear performance of Al-, mica-, SiO2-filled glass fiber-reinforced composites and prediction of wear properties with artificial neural networks," In Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, p. 09544089241307840, 2 Jan. 2025, doi: 10.1177/095440892413078
- [11] M.E. Demir, and R.K. Ergün, "Mechanical and wear performance of Al-, mica-, SiO2-filled glass fiber-reinforced composites and prediction of wear properties with artificial neural networks," In Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, p. 09544089241307840, 2 Jan. 2025, doi: 10.1177/095440892413078.
- [12] Demir, M. E., Çetkin, E., Ergün, R. K., & Denizhan, O. (2024). Tribological and mechanical properties of nanofilled glass fiber reinforced composites and analyzing the tribological behavior using artificial neural networks. Polymer Composites, 45(5), 4233–4249. https://doi.org/10.1002/pc.28055
- [13] Demir, M. E. (2025). The effect of filler type (tungsten carbide, zinc oxide) and content on the mechanical and wear behavior of jute/flax reinforced epoxy hybrid composites: Experimental and artificial neural network analysis. Polymer Composites. https://doi.org/10.1002/pc.29879
- [14] Gürbüz, H., Demir, M. E., Baday, Ş., & Akcan, İ. H. (2025). Yapay sinir ağı ile mikro parçacık dolgulu kompozitlerin tribolojik özelliklerinin tahmini. Journal of Materials and Mechatronics: A, 6(1), 68–82. https://doi.org/10.55546/jmm.1581662
- [15] Denizhan, O. (2025). Application of various artificial neural network algorithms for regression analysis in the dynamic modeling of a three-link planar RPR robotic arm. Machines, 13(11), 1031. https://doi.org/10.3390/machines13111031
- [16] Denizhan, O. (2024). Comparison of different supervised learning algorithms for position analysis of the slider-crank mechanism. Alexandria Engineering Journal, 92, 39–49. https://doi.org/10.1016/j.aej.2024.02.055
- [17] Kong, F. G., Li, Q., & Zhang, W. J. (1999). An artificial neural network approach to mechanism kinematic chain isomorphism identification. Mechanism and Machine Theory, 34(2), 271–283. https://doi.org/10.1016/S0094-114X(98)00035-4
- [18] Efe, M. O., & Kaynak, O. (2000). Stabilizing and robustifying the learning mechanisms of artificial neural networks in control engineering applications. International Journal of Intelligent Systems, 15(5), 365–388. https://doi.org/10.1002/(SICI)1098-111X(200005)15:5
- [19] Yang, Z., Wu, J., & Mei, J. (2007). Motor-mechanism dynamic model based neural network optimized computed torque control of a high speed parallel manipulator. Mechatronics, 17(7), 381–390. https://doi.org/10.1016/j.mechatronics.2007.04.009
- [20] Cui, H. (2025). High-dimensional learning of narrow neural networks. Journal of Statistical Mechanics: Theory and Experiment, 2025(2), 023402. https://doi.org/10.1088/1742-5468/adb1d6
- [21] Beise, H. P., Da Cruz, S. D., & Schröder, U. (2021). On decision regions of narrow deep neural networks. Neural Networks, 140, 121–129. https://doi.org/10.1016/j.neunet.2021.02.024
- [22] Lu, L., Su, Y., & Karniadakis, G. E. (2018). Collapse of deep and narrow neural nets. arXiv preprint arXiv:1808.04947. https://doi.org/10.48550/arXiv.1808.04947
- [23] Babu, M. V., & Banana, K. (2024). A study on narrow artificial intelligence—An overview. International Journal of Engineering Science and Advanced Technology, 24(4), 210–219
- [24] Alali, M., Sharef, N. M., Murad, M. A., Hamdan, H., & Husin, N. A. (2019). Narrow convolutional neural network for Arabic dialects polarity classification. IEEE Access, 7, 96272–96283. https://doi.org/10.1109/ACCESS.2019.2929208
- [25] Lee, H., Kim, Y., Yang, S. Y., & Choi, H. (2024). Improved weight initialization for deep and narrow feedforward neural network. Neural Networks, 176, 106362. https://doi.org/10.1016/j.neunet.2024.106362
- [26] Dommel, J., & Wegner, S. A. (2025). An in-depth look at approximation via deep and narrow neural networks. Neurocomputing. https://doi.org/10.1016/j.neucom.2025.131769
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Yazılım Testi, Doğrulama ve Validasyon |
| Bölüm | Araştırma Makalesi |
| Yazarlar | |
| Proje Numarası | N/A |
| Gönderilme Tarihi | 12 Şubat 2026 |
| Kabul Tarihi | 26 Mart 2026 |
| Yayımlanma Tarihi | 27 Mart 2026 |
| DOI | https://doi.org/10.17694/bajece.1887866 |
| IZ | https://izlik.org/JA29ED98EY |
| Yayımlandığı Sayı | Yıl 2026 Cilt: 14 |
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BAJECE is committed to following the Code of Conduct and Best Practice Guidelines of COPE (Committee on Publication Ethics) . It is a duty of our editors to follow Cope Guidance for Editors and our peer-reviewers must follow COPE Ethical Guidelines for Peer Reviewers .
If you have any questions, please contact the relevant editorial office, or Balkan Journal of Electrical and Computer Engineering (BAJECE)' ethics representative: bajece@hotmail.com
Download a PDF version of the Ethics and Policies [PDF,392KB].
Reviewer Process Information
BAJECE employs a single-blind peer review process to ensure scientific quality, fairness, and transparency. In this review model, reviewers are able to see the authors’ names and affiliations, while authors do not have access to the reviewers’ identities. This approach allows reviewers to provide objective, detailed, and constructive feedback while maintaining their anonymity.
All submitted manuscripts are first evaluated by the Editorial Board for relevance, structure, and adherence to journal guidelines. Papers that meet the initial criteria are then assigned to at least two independent reviewers who are experts in the related research area. Reviewers assess manuscripts based on originality, technical accuracy, clarity, methodology, and scientific contribution.
Authors are required to revise their papers according to reviewers’ comments and suggestions within the given time frame. The final publication decision—acceptance, revision, or rejection—is made by the Editor-in-Chief after considering the reviewers’ recommendations and the scientific merit of the manuscript.
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