IDEALIZATION OF DATA, FUNDAMENTAL CHANGE IN NEURAL NETWORK DESIGN

该项目旨在更好地了解神经网络如何 操作，以便自动呈现新收集的 光谱到相应数据库的搜索引擎。 我们有 开发了一种使用神经网络的新方法， 从根本上改变目前的方法。 与新 方法化学签名（光谱）可以呈现 直接到神经网络，无需进行任何预处理 减少噪音、校正基线或消除污染物的步骤 并且在分类精度上没有任何损失。 该技术具有 已成功应用于CCRC-Net系统开发搜索 木葡聚糖低聚糖和小N-连接 寡糖数据库。 我们今年的研究工作 建立了小型数据库，如CCRC-Net的木葡聚糖 寡糖数据库，线性技术，如相关性 系数分析足以确定木葡聚糖 在光谱被理想化之后的结构。 然而，对于大 N-连接寡糖数据库，一种非线性分离技术 需要一个相对较小的神经网络， 来识别结构。 正在准备一份手稿， 这项工作 我们已于一九九九年为这项计划申请独立拨款。 美国国立卫生研究院的RO 1补助金。 一项专利申请也正在 为这项工作做好准备。

This project seeks to better understand how neural networks operate in order to automate the presentation of newly collected spectra to the search engine of the corresponding database. We have developed a new methodology for using neural networks that represents a fundamental shift from current methodology. With the new methodology the chemical signature (spectrum) can be presented directly to the neural networks without conducting any pre-processing steps to reduce noise, correct the baseline, or eliminate contaminants and without any loss in classification accuracy. This technology has been successfully applied to the CCRC-Net system to develop search engines for the xyloglucan oligosaccharide and the small N-linked oligosaccharide databases. Our research work this year has established that for small databases, such as CCRC-Net's xyloglucan oligosaccharide database, a linear technique such as correlation coefficient analysis is sufficient to determine the xyloglucan structure after the spectrum has been idealized. However, for large N-linked oligosaccharide databases, a non-linear separation technique is required, such as a relatively small-sized neural network, in order to identify the structure. A manuscript is in preparation describing this work. We have applied for separate funding for this project in the form of an RO1 grant from NIH. A patent application is also being prepared for this work.