Advertisement

Simulation of Learning Logical Functions Using Single-Layer Perceptron

  • Mohd Vasim Ahamad
  • Rashid Ali
  • Falak Naz
  • Sabih Fatima
Conference paper
  • 18 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1097)

Abstract

As a simplest neural network, the perceptron computes a linear combination of real-valued labeled training samples and predicts the classes for unclassified testing samples. If two different sets of samples can be separated by a straight line, they are called linearly separable. The perceptron can be considered as the binary classifier of unclassified samples based on the supervised machine learning approach. The learning algorithm for the perceptron takes a static value for the learning rate as an input, which can affect the efficiency of the learning process. This work attempts to implement logical operations OR, AND, NOR, and NAND using a single-layer perceptron algorithm. A modified perceptron algorithm is proposed which finds the most optimal value of learning for the learning process. The learning algorithm is provided with a range of learning rate values, and it picks the most suitable learning rate by calculating the number of iterations taken by each of these learning rates and choosing the one which takes the minimum number of the epoch.

Keywords

Artificial neural network Perceptron Linear classifier Learning rate Artificial neuron 

References

  1. 1.
    Ahamad, Mohd Vasim, Misbah Urrahman Siddiqui, Tariq Ahmed, and Asia Mashkoor. 2017. Clustering and classification algorithms in data mining. International Journal of Advance Research in Science and Engineering 6 (7): 1110–1117.Google Scholar
  2. 2.
    Yanling, Zhao, Deng Bimin, and Wang Zhanrong. 2002. Analysis and study of perceptron to solve XOR problem. In Proceedings of the 2nd International Workshop on Autonomous Decentralized System. ISBN: 0-7803-7624-2/02.Google Scholar
  3. 3.
    Khalil Alsmadi, Mutasem, Khairuddin Bin Omar, Shahrul Azman Noah, and Ibrahim Almarashdah. 2009. Performance comparison of multi-layer perceptron (back propagation, delta rule and perceptron) algorithms in neural networks. In IEEE International Advance Computing Conference (IACC 2009), Patiala, India. ISBN: 978-1T-4244-1888-6/08.Google Scholar
  4. 4.
    Melnychuk, Stepan, Mykola Kuz, and Serhiy Yakovyn. 2018. Emulation of logical functions NOT, AND, OR, and XOR with a perceptron implemented using an information entropy function. In 14th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET-18), Lviv-Slavske, Ukraine. ISBN: 978-1-5386-2556-9/18.Google Scholar
  5. 5.
    Naraei, Parisa, Abdolreza Abhari, and Alireza Sadeghian. 2016. Application of multilayer perceptron neural networks and support vector machines in classification of healthcare data. In 2016 Future Technologies Conference (FTC), 6–7 Dec 2016, San Francisco, USA. ISBN: 978-1-5090-4171-8/16.Google Scholar
  6. 6.
    Saji, Sumi Alice, and K. Balachandran 2015. Performance analysis of training algorithms of multilayer perceptrons in diabetes prediction. In 2015 International Conference on Advances in Computer Engineering and Applications (ICACEA) IMS Engineering College, Ghaziabad, India. ISBN: 978-1-4673-6911-4/15.Google Scholar
  7. 7.
    Zubir, N.S.A., M.A. Abas, Nurlaila Ismail, Nor Azah M. Ali, M.H.F. Rahiman, N.K. Mun, N.T. Saiful, and M.N. Taib. 2017. Analysis of algorithms variation in multilayer perceptron neural network for agarwood oil qualities classification. In IEEE 8th Control and System Graduate Research Colloquium, Aug 2017, Shah Alam, Malaysia. ISBN: 978-1-5386-0380-2/17.Google Scholar
  8. 8.
    Ravichandran, K., and S. Arulchelvan. 2017. The model of multilayer perceptron analysed the crime news awareness in India. In International Conference on Advanced Computing and Communication Systems. ISBN: 978-1-5090-4559-4/17.Google Scholar
  9. 9.
    Mubarek, Aji Mubalaike, Esref Adali. 2017. Multilayer perceptron neural network technicque for fraud detection. In 2nd International Conference on Computer Science and Engineering 383–387. ISBN: 978-1-5386-0930-9/17.Google Scholar
  10. 10.
    Hasan, Ali T., Hayder M.A.A. Al-Assadi, and Ahmad Azlan Mat Isa. 2011. Neural networks’ based inverse kinematics solution for serial robot manipulators passing through singularities. Artificial Neural Networks—Industrial and Control Engineering Applications, Kenji Suzuki, IntechOpen.  https://doi.org/10.5772/14977.
  11. 11.
    Merikle, Philip M., D. Smilek, and John D. Eastwood. 2001. Perception without awareness: perspectives from cognitive psychology. Science Direct 79 (1–2): 115–134.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Mohd Vasim Ahamad
    • 1
  • Rashid Ali
    • 1
  • Falak Naz
    • 1
  • Sabih Fatima
    • 2
  1. 1.Department of Computer EngineeringZHCET, Aligarh Muslim UniversityAligarhIndia
  2. 2.Department of Electrical EngineeringZHCET, Aligarh Muslim UniversityAligarhIndia

Personalised recommendations