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Efficient methods of initializing neuron weights in self-organizing networks implemented in hardware

Author

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  • Kolasa, Marta
  • Długosz, Rafał
  • Talaśka, Tomasz
  • Pedrycz, Witold

Abstract

In this paper, we focus on the topic of an efficient initialization of neuron weights, which is one of key problems in artificial neural networks (ANNs). This problem is important in ANNs implemented as Application Specific Integrated Circuits (ASICs), in which the number of the weights is relatively large. When ANNs are implemented in software, the weights can be easily modified. In contrast, in neural networks realized as ASICs in which due to parallel data processing each neuron is realized as a separate circuit, it is necessary to provide programming and addressing lines to each memory cell containing a weight. This causes a substantial increase in the complexity of such systems. In this study, we performed comprehensive investigations, in which we simulated the training process of the Self-Organizing ANN with different initialization scenarios. The aim of these investigations was to find simple and efficient initialization procedures that lead to optimal learning process for a broad spectrum of values of other network parameters.

Suggested Citation

  • Kolasa, Marta & Długosz, Rafał & Talaśka, Tomasz & Pedrycz, Witold, 2018. "Efficient methods of initializing neuron weights in self-organizing networks implemented in hardware," Applied Mathematics and Computation, Elsevier, vol. 319(C), pages 31-47.
    • Handle: RePEc:eee:apmaco:v:319:y:2018:i:c:p:31-47
    DOI: 10.1016/j.amc.2017.01.043
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    References listed on IDEAS

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    1. Talaśka, Tomasz & Kolasa, Marta & Długosz, Rafał & Farine, Pierre-André, 2015. "An efficient initialization mechanism of neurons for Winner Takes All Neural Network implemented in the CMOS technology," Applied Mathematics and Computation, Elsevier, vol. 267(C), pages 119-138.
    2. Kolasa, Marta & Talaska, Tomasz & Długosz, Rafał, 2015. "A novel recursive algorithm used to model hardware programmable neighborhood mechanism of self-organizing neural networks," Applied Mathematics and Computation, Elsevier, vol. 267(C), pages 314-328.
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    Cited by:

    1. Marta Kolasa, 2020. "An Energy-Efficient, Parallel Neighborhood and Adaptation Functions for Hardware Implemented Self-Organizing Maps Applied in Smart Grid," Energies, MDPI, vol. 13(5), pages 1-25, March.

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