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Performance of Affordable Neural Network for Back Propagation Learning

机译:用于反向传播学习的经济实惠的神经网络的性能

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摘要

Cell assembly is one of explanations of information processing in the brain, in which an information is represented by a firing space pattern of a group of plural neurons. On the other hand, effectiveness of neural network has been confirmed in pattern recognition, system control, signal processing, and so on, since the back propagation learning was proposed. In this study, we propose a new network structure with affordable neurons in the hidden layer of the feedforward neural network. Computer simulated results show that the proposed network exhibits a good performance for the back propagation learning. Furthermore, we confirm the proposed network has a good generalization ability.
机译:细胞组装是对大脑中信息处理的解释之一,其中信息由一组复数神经元的放电空间模式表示。另一方面,自从反向传播学习被提出以来,神经网络的有效性在模式识别、系统控制、信号处理等方面得到了证实。在这项研究中,我们提出了一种新的网络结构,在前馈神经网络的隐藏层中具有可负担的神经元。计算机仿真结果表明,所提网络在反向传播学习方面表现出良好的性能。此外,我们确认所提出的网络具有良好的泛化能力。

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