Open Access


Performance Enhancement of Adaptive Neural Networks Based on Learning Rate

Swaleha Zubair1, Anjani Kumar Singha1, Nitish Pathak2, Neelam Sharma3, Shabana Urooj4,*, Samia Rabeh Larguech4
1 Department of Computer Science, Aligarh Muslim University, Aligarh, 202002, Uttar Pradesh, India
2 Department of Information Technology, Bhagwan Parshuram Institute of Technology (BPIT), GGSIPU, New Delhi, 110089, India
3 Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology (MAIT), GGSIPU, New Delhi, India
4 Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi Arabia
* Corresponding Author: Shabana Urooj. Email:

Computers, Materials & Continua 2023, 74(1), 2005-2019.

Received 19 April 2022; Accepted 15 June 2022; Issue published 22 September 2022


Deep learning is the process of determining parameters that reduce the cost function derived from the dataset. The optimization in neural networks at the time is known as the optimal parameters. To solve optimization, it initialize the parameters during the optimization process. There should be no variation in the cost function parameters at the global minimum. The momentum technique is a parameters optimization approach; however, it has difficulties stopping the parameter when the cost function value fulfills the global minimum (non-stop problem). Moreover, existing approaches use techniques; the learning rate is reduced during the iteration period. These techniques are monotonically reducing at a steady rate over time; our goal is to make the learning rate parameters. We present a method for determining the best parameters that adjust the learning rate in response to the cost function value. As a result, after the cost function has been optimized, the process of the rate Schedule is complete. This approach is shown to ensure convergence to the optimal parameters. This indicates that our strategy minimizes the cost function (or effective learning). The momentum approach is used in the proposed method. To solve the Momentum approach non-stop problem, we use the cost function of the parameter in our proposed method. As a result, this learning technique reduces the quantity of the parameter due to the impact of the cost function parameter. To verify that the learning works to test the strategy, we employed proof of convergence and empirical tests using current methods and the results are obtained using Python.


Deep learning; optimization; convergence; stochastic gradient methods

Cite This Article

S. Zubair, A. K. Singha, N. Pathak, N. Sharma, S. Urooj et al., "Performance enhancement of adaptive neural networks based on learning rate," Computers, Materials & Continua, vol. 74, no.1, pp. 2005–2019, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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