基于web的分布式模式识别系统

Sung-Jung Hsiao, Wen-Tsai Sung, Kuo-Chin Fan
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引用次数: 3

摘要

本研究试图将神经网络原理和模式识别技术应用于客户端-服务器网络结构的实时识别,构建基于web的识别系统。本文介绍了一种基于web的PR技术,该技术是改进的递归神经网络(RNN),其输入具有反馈和非线性激活函数,并去除阈值。本文的目的是。构建具有联想记忆的PR系统的Client-Server网络结构。服务器端建立了数据库管理系统,用于存储样本模式。在进行训练时,用户可以实时分配任何模式,这是服务器端数据库中的记录。在处理检索任务时,我们提出了一种新的基于数据库匹配的检索方法,它可以有效地解决WBPR系统中来自RNN的虚假状态问题。另一方面,利用数据库匹配是为了克服RNN的容量限制。为了澄清和证实上述基于web的公关技术,本文将进行仿真实验,并对其算法进行讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Web-based distributed pattern recognition system
This study attempts to apply the principle of neural networks and pattern recognition (PR) technologies to real-time recognition by client-server network structure into a web-based recognizing system. In this paper, we recommend a Web-Based PR technology, which is improved recurrent neural network (RNN) from possessing feedback and non-linear activation function with its input, be taken out threshold. The purpose of this article is. to construct a Client-Server network structure for PR system with associative memory. The Server-end is built a databases management system for storage sample patterns. In proceed with training, the user can real-time assign any pattern, which is a record in the Server-end databases. In deal with retrieve task, we propose a novel PR method via databases matching, it can efficient solve spurious states problem from RNN in the WBPR system. On the other hand, taking advantage of Database Matching is to overcome the capacity restrictions on RNN. In order to clarify and corroborate the above Web-Based PR technology, thus a simulation experiment will be presented and their algorithms are also discussed.
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