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首页> 外文期刊>Journal of supercomputing >The weights initialization methodology of unsupervised neural networks to improve clustering stability
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The weights initialization methodology of unsupervised neural networks to improve clustering stability

机译：无监督神经网络的权重初始化方法，提高聚类稳定性

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摘要

A study on initialization of connection weights of neural networks is expected to be needed because various deep neural networks based on deep learning have attracted much attention recently. However, studies on the relation between the output value of the active function and the learning performance of the neural network with respect to the connection weight value have been conducted mainly on the supervised learning model. This paper focused on improving the efficiency of autonomous neural network model by studying the connection weight initialization as the neural network model of supervised learning. Adaptive resonance theory (ART) is a major model of autonomous neural network that tries to solve the stability-plasticity dilemma by using bottom-up weights and top-down weights. The conventional weights initialization method of ART was to uniformly set all weights, but the proposed method is to initialize by using pre-trained weights. Experiments show that the ART, which initializes the connectivity weights through the proposed method, performs clustering more reliably.
机译：预计基于深度学习的各种深度神经网络最近引起了很多关注的初始化神经网络连接权重的研究。然而，关于主动函数的输出值与神经网络的学习性能相对于连接权重值之间的关系的研究主要是在监督学习模型上进行的。本文专注于通过研究连接权重初始化作为监督学习神经网络模型来提高自主神经网络模型的效率。自适应共振理论（ART）是自主神经网络的主要模型，其试图通过使用自下而上的重量和自上而下的重量来解决稳定性塑性困境。常规权重初始化方法是均匀地设定所有权重，但是所提出的方法是通过使用预先训练的重量来初始化。实验表明，通过所提出的方法初始化连接权重的本领域更可靠地执行聚类。

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