Abstract
Though the Artificial Neural Network is used as a potential tool to solve many of the real-world learning and adaptation problems, the research articles revealing the simple facts of how to simulate an artificial neuron for most popular tasks are very scarce. This paper has in its objective presenting the details of design and implementation of artificial neurons for linearly separable Boolean functions. The simple Boolean functions viz AND and OR are taken for the study. This paper initially presents the simulation details of artificial neurons for AND and OR operations, where the required weight values are manually calculated. Next, the neurons are added with learning capability with perceptron learning algorithm and the iterative adaptation of weight values are also presented in the paper.
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Raj, K.S., Nishanth, M., Jeyakumar, G. (2020). Design of Binary Neurons with Supervised Learning for Linearly Separable Boolean Operations. In: Smys, S., Tavares, J., Balas, V., Iliyasu, A. (eds) Computational Vision and Bio-Inspired Computing. ICCVBIC 2019. Advances in Intelligent Systems and Computing, vol 1108. Springer, Cham. https://doi.org/10.1007/978-3-030-37218-7_54
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DOI: https://doi.org/10.1007/978-3-030-37218-7_54
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