一种使用多权重和偏差集的模式识别神经网络

Le Dung, M. Mizukawa
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引用次数: 9

摘要

在监督训练中,我们经常试图为模式识别神经网络找到一组权值和偏差,以便对训练数据集中的所有模式进行分类。然而,如果神经网络不够大,无法学习大型训练数据集,这将是困难的。在本文中,我们提出了一种训练方法和一种模式识别神经网络的设计,该神经网络体积不大,但仍能准确地对所有训练模式进行分类。神经网络设计了一个拒绝输出,将训练数据集分割成多个部分,便于分类。该训练方法帮助神经网络找到不止一组而是多组的权值和偏差来对所有训练模式进行分类,控制识别拒绝,降低错误率。另一方面,通过这种设计,我们可以减少在FPGA芯片上实现的神经网络的大小,以便为机器人制造快速的智能传感器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Pattern Recognition Neural Network Using Many Sets of Weights and Biases
In supervised training, we often try to find out a set of weights and biases for a pattern recognition neural network in order to classify all patterns in a training data set. However, it would be difficult if the neural network was not big enough for learning a large training data set. In this paper, we propose a training method and a design of pattern recognition neural network that is not big but still able to classify all the training patterns exactly. The neural network is designed with a reject output to separate the training data set into some parts for classifying more easily. The training method helps the neural network to find out not only one but many sets of weights and biases for classifying all the training patterns, controlling the recognizing rejection and reducing the error rate. On the other hand, with this design we can reduce the size of the neural network implemented on a FPGA chip in order to make fast smart sensors for the robots.
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