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Computational Study on the Structure and Learning in the Integrated Neural Networks for Different Sensors

不同传感器的集成神经网络结构和学习的计算研究

基本信息

批准号：
09680365
负责人：
ISHII Naohiro
金额：
$ 2.3万
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

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-09680365/
关键词：
Neural network Asymmetric network Structure of network Function of network Nonlinear analysis Integrated network 異種センサーネットワークの構造追加学習

项目摘要

Biological neural networks play important roles in the sensory infor- mation processing, which is different from artificial neural networks. The retinal neural networks in the visual system, are classified into symmetrical and asymmetrical structures. The asymmetrical networks, which are shown in catfish and cat et al., consist of the linear pathway and the nonlinear pathway. This asymmetrical networks have characteristic properties in the sensory perceptions.The asymmetric structures reflect the function of the neural networks. Thus, this study, first analyzes what is the functions of the asymmetric neural networks in the sensory perception. Second, the integrated neural netwoks are discussed to respond and learn different sensory informations from different perceptual networks. The integrated network consist of several sub-netwoks to recognize objects correctly by using several sensors."Forward Network" receives inputs from corresponding sensor. "Integrating Unit" integrates outputs of all. Forward Networks."Backward Network" receives inputs from Integrating Unit and recostructs the sensory information as its outputs. In the recognition phase, the integ- rated system gets a correct output by modifying the inputs of Forward Networks, the output of Integrating Unit and the confidence of the sensory information repeatedly to maximize a likelihood function , which can be derived by a Bayesian method. It was clarified the system has the improved ability in the integrated networks.

与人工神经网络不同，生物神经网络在感觉信息处理中发挥着重要作用。视觉系统中的视网膜神经网络分为对称结构和非对称结构。在catfish和cat等人的研究中，由线性路径和非线性路径组成。这种非对称网络在感觉知觉中具有独特的性质，其非对称结构反映了神经网络的功能。因此，本研究首先分析了非对称神经网络在感觉知觉中的作用。其次，讨论了整合神经网络对不同感知网络的感知信息的反应和学习。该集成网络由多个子网络组成，通过使用多个传感器来正确识别目标。“前向网络”接收来自相应传感器的输入。“整合股”整合了所有部门的产出。Forward Networks.“后向网络”接收来自整合单元的输入，并将感觉信息重构为其输出。在识别阶段，集成系统通过反复修改前向网络的输入、集成单元的输出和感知信息的置信度，以最大化一个似然函数，从而得到一个正确的输出。结果表明，该系统在综合网络中具有较好的应用能力.