A Neural Network Model with interaction between Extraneous Information and Internal Memory

外部信息与内部记忆相互作用的神经网络模型

基本信息

批准号：
09650465
负责人：
IWATA Akira
金额：
$ 2.11万
依托单位：
Nagoya Institute of Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1997
资助国家：
日本
起止时间：
1997 至 1998
项目状态：
已结题

项目摘要

In this study, we focused on the memory system of the human being and constructed a incremental learning neural network model for the symbolic patterns. The network model has a hierarchical structure and each layer has neuron blocks which recognize local features of the lower layer's output pattern. The network model does bottom-up processing and finally recognize the input pattern as the integrated information of the local features. Each neuron block is controlled by a learning trigger signal and can learn the lower layer's patterns independently. Therefore, if there are partial differences between the input pattern and the learned patterns, the network can learn only the differences between them incrementally. In the case of dealing with multi-sense information, the network model can learn only the partial information which the network model have not learned yet. In the study, we constructed the incremental learning network model. And we showed the learning ability of the model by simulations. In the first work, we tested the model by using 26 patterns as the distributed patterns that imitated the alphabet characters. As the result of the simulation, we showed the model could do incremental learning all of the patterns and recognize them after the learning process. And then, we used 200 patterns imitated the kanji characters which had "hen" and "tsukuri" structures. Each input pattern had a combination of "hen" pattern and "tsukuri" pattern, and there were about 50 "hen" patterns and 50 "tsukuri" patterns in the 200 input patterns. As the result of the simulations, the model could learn all of 200 patterns and learn only the partial differences between the "hen" or "tsukuri" patterns. We presented the above results at the IEICE meetings of technical group NC98 (Mar.1998)

在这项研究中，我们专注于人类的记忆系统，并为符号模式构建了增量学习神经网络模型。网络模型具有层次结构，每个层都有神经元块，可识别下层输出模式的局部特征。网络模型进行自下而上的处理，并最终将输入模式视为本地特征的集成信息。每个神经元块都由学习触发信号控制，并且可以独立学习下层的模式。因此，如果输入模式与学习模式之间存在部分差异，则网络只能逐步学习它们之间的差异。在处理多态信息的情况下，网络模型只能学习网络模型尚未学到的部分信息。在研究中，我们构建了增量学习网络模型。我们通过模拟展示了模型的学习能力。在第一项工作中，我们通过使用26个模式作为模仿字母字符的分布式模式测试了模型。作为模拟的结果，我们表明该模型可以进行所有模式的增量学习，并在学习过程后识别它们。然后，我们使用了200种模式，模仿了具有“母鸡”和“ Tsukuri”结构的汉字字符。每个输入模式都有“母鸡”模式和“ tsukuri”模式的组合，并且在200个输入模式中大约有50个“母鸡”模式和50个“ tsukuri”模式。作为模拟的结果，该模型可以学习所有200种模式，并仅学习“ Hen”或“ Tsukuri”模式之间的部分差异。我们在技术集团NC98（1998年3月）的IEICE会议上介绍了上述结果