Recognizing handwritten digits using spiking neural networks with learning algorithms based on sliding mode control theory

Authors

YEŞİM ÖNİZ
MEHMET AYYILDIZ

DOI

10.55730/1300-0632.4022

Abstract

In this paper, a spiking neural network (SNN) has been proposed for recognizing the digits written on the LCD screen of an experimental setup. The convergence of the learning algorithm has been ensured by using sliding mode control (SMC) theory and the Lyapunov stability method for the adaptation of the network parameters. The spike response model (SRM) has been utilized in the design of the SNN. The performance of the proposed learning scheme has been evaluated both on the experimental data and on the MNIST dataset. The simulated and experimental results of the SNN structure have been compared with the responses of a conventional neural network (ANN) for which the weight update rules have been also derived using SMC theory. The conducted simulations and experimental studies reveal that convergence can be ensured for the proposed learning scheme and the SNN yields higher recognition accuracy compared to a conventional ANN.

Keywords

Digit recognition, spiking neural networks, sliding mode control

First Page

860

Last Page

875

Recommended Citation

ÖNİZ, YEŞİM and AYYILDIZ, MEHMET (2023) "Recognizing handwritten digits using spiking neural networks with learning algorithms based on sliding mode control theory," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 31: No. 5, Article 8. https://doi.org/10.55730/1300-0632.4022
Available at: https://journals.tubitak.gov.tr/elektrik/vol31/iss5/8