Analysis of Multiple Hidden Layer vs. Accuracy in Performance using Back Propagation Neural Network

Asthana, Stuti; Pandit, Amitkant; Bhardwaj, Anil

doi:10.17485/ijst/2017/v10i4/110899

Cited by 3 publications

(1 citation statement)

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“…Set the number of hidden layers to 5 (Figure 4c). According to the publication of Asthana et al., [26] the prediction results are better when the number of hidden layers is 5. The activation functions performed in the hidden layer and output layer are “ tansig ” and “ purelin ” functions, attributing to better performance during the training process [27] …”

Section: Machine Learning (Ml) and Prediction Strategymentioning

confidence: 97%

A comparative study of machine learning in predicting the mechanical properties of the deposited AA6061 alloys via additive friction stir deposition

Qiao,

Liu,

et al. 2024

Materials Genome Engineering Advances

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Additive friction stir deposition (AFSD) provides strong flexibility and better performance in component design, which is controlled by the process parameters. It is an essential and difficult task to tune those parameters. The recent exploration of machine learning (ML) exhibits great potential to obtain a suitable balance between productivity and set parameters. In this study, ML techniques, including support vector machine (SVM), random forest (RF) and artificial neural network (ANN), are applied to predict the mechanical properties of the AFSD‐based AA6061 deposition. Expect for the stable parameters (temperature, force and torque) in situ monitored by the self‐developed process‐aware kit during the AFSD process and the other factors (rotation speed, traverse speed, feed rate and layer thickness) are also set as input variables. The output variables are microhardness and ultimate tensile strength (UTS). Prediction results show that the ANN model performs the best prediction accuracy with the highest R2 (0.9998) and the lowest mean absolute error (MAE, 0.0050) and root mean square error (RMSE, 0.0063). Furthermore, analysis suggests that the feed rate (24.8%/24.1%) and layer thickness (25.6%/26.6%) indicate a higher contribution that affects the mechanical properties.

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Section: Machine Learning (Ml) and Prediction Strategymentioning

confidence: 97%

A comparative study of machine learning in predicting the mechanical properties of the deposited AA6061 alloys via additive friction stir deposition

Qiao,

Liu,

et al. 2024

Materials Genome Engineering Advances

View full text Add to dashboard Cite

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Effects of Hidden Layers on the Efficiency of Neural networks

Uzair

Jamil

2020

2020 IEEE 23rd International Multitopic Conference (INMIC)

145

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Hidden layers play a vital role in the performance of Neural network especially in the case of complex problems where the accuracy and the time complexity are the main constraints. The process of deciding the number of hidden layers and number of neurons in each hidden layer is still confusing. In this article, we reviewed different impacts of Hidden layers on the network which provides an overview of using three numbers of hidden layers that were found to be optimal in terms of reducing the time complexity and getting the qualified accuracy. The techniques implementing less than three number of hidden layers mostly had a loss in accuracy while the architecture implementing more than three numbers of hidden layers were found not to be optimal in terms of time complexity. Usually implementing three numbers of hidden layers give the optimal performance in terms of time complexity and accuracy. We had a survey on recent work about the Neural network based on the Empirical observations, in which if the number of hidden layers is reduced it has a direct impact on the accuracy of the network as with the complex problem having less number of hidden layers it might be possible that network will not be trained properly. On the other hand when the number of hidden layers cross the optimal number of hidden layers (three layers), time complexity increases in orders of magnitude as compared to the accuracy gain.

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Enhancing Prosthetic Control: Neural Network Classification of Thumb Muscle Contraction Using HD-sEMG Signals

Suhaimi,

Ghazali,

Haja Mohideen

et al. 2024

IIUMEJ

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The progression of prosthetic technology, enabling precise thumb control and movement, has reached a stage where noninvasive techniques for capturing bioelectrical signals from muscle activity are preferred over alternative methods. While electromyography's applications extend beyond just interfacing with prostheses, this initial investigation delves into evaluating various classifiers' accuracy in identifying rest and contraction states of the thumb muscles using extrinsic forearm readings. Employing a High-Density Surface Electromyogram (HD-sEMG) device, bioelectrical signals generated by muscle activity, detectable from the skin's surface, were transformed into contours. A training system for the thumb induced muscle activity in four postures: 0°, 30°, 60°, and 90°. The collection of HD-sEMG signals originating from both the anterior and posterior forearms of seventeen participants has been proficiently classified using a neural network with 100% accuracy and a mean square error (MSE) of 1.4923 x 10-5 based on the testing dataset. This accomplishment in classification was realized by employing the Bayesian regularization backpropagation (trainbr) training technique, integrating seven concealed layers, and adopting a training-validation-testing proportion of 70-15-15. In the realm of future research, an avenue worth exploring involves the potential integration of real-time feedback mechanisms predicated on the recognition of thumb muscle contraction states. This integration could offer an enhanced interaction experience between users and prosthetic devices. ABSTRAK: Perkembangan teknologi prostetik mengguna pakai kaedah selamat iaitu isyarat bioelektrikal yang diperoleh dari pergerakan otot lebih digemari digunakan berbanding kaedah alternatif. Ini membolehkan kawalan dan pergerakan ibu jari dengan tepat. Sementara aplikasi elektromiografi telah melangkah jauh melebihi antara muka prostesis. Kajian awal ini mengkaji pelbagai ketepatan klasifikasi dalam mengenal pasti keadaan rehat dan kontraksi otot ibu jari menggunakan bacaan lengan bawah ekstrinsik. Dengan menggunakan peranti Elektromiogram Permukaan Kepadatan-Tinggi (HD-sEMG), isyarat bioelektrikal yang terhasil dari pergerakan otot, boleh ditanggalkan dari permukaan kulit, di ubah kepada kontur. Sistem latihan pada ibu jari menghasilkan pergerakan otot dalam empat postur iaitu: 0°, 30°, 60°, dan 90°. Isyarat terkumpul dari HD-sEMG berasal dari kedua-dua lengan tangan anterior dan posterior dari 17 peserta telah diklasifikasi dengan cekap menggunakan rangkaian neural dengan ketepatan 100% dan min kuasa dua ralat (MSE) sebanyak 1.4923 x 10-5 berdasarkan setdata yang diuji. Klasifikasi sempurna ini dicapai dengan menggunakan teknik latihan aturan rambatan-belakang Bayesian (trainbr), mengguna pakai tujuh lapisan tersembunyi dengan gabungan latihan-validasi-ujian mengikut kadar 70-15-15. Pada masa hadapan, pengkaji boleh menerokai potensi integrasi mekanisme tindak balas nyata dalam meramal dan mengenali kontraksi otot ibu jari. Integrasi ini mungkin membolehkan pengalaman interaksi antara peranti prostetik dan pengguna.

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Analysis of Multiple Hidden Layer vs. Accuracy in Performance using Back Propagation Neural Network

Cited by 3 publications

References 0 publications

A comparative study of machine learning in predicting the mechanical properties of the deposited AA6061 alloys via additive friction stir deposition

A comparative study of machine learning in predicting the mechanical properties of the deposited AA6061 alloys via additive friction stir deposition

Effects of Hidden Layers on the Efficiency of Neural networks

Enhancing Prosthetic Control: Neural Network Classification of Thumb Muscle Contraction Using HD-sEMG Signals

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