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

Marta Kolasa, Rafał Długosz, Tomasz Talaśka and Witold Pedrycz

Applied Mathematics and Computation, 2018, vol. 319, issue C, 31-47

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.

Keywords: Self-Organizing Maps; Initialization of neuron weights; CMOS implementation; Programmable circuits (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:319:y:2018:i:c:p:31-47

DOI: 10.1016/j.amc.2017.01.043

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