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首页> 外文会议>Conference on Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2003 Apr 23-25, 2003 Orlando, Florida, USA >A Neurophysiological Paradigm for Data Fusion in a Multisource Environment
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A Neurophysiological Paradigm for Data Fusion in a Multisource Environment

机译：多源环境中数据融合的神经生理学范式

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摘要

A technique for representing data obtained from sensors, video streams imagery, sound, text, etc. is presented. The technique is called Finite Inductive Sequences (FI) and is proposed as a means for eliminating data requiring storage where conventional mathematical models don't eliminate enough and statistical models eliminate too much. FI is a simple idea and is based upon a symbol push-out technique that allows the order (inductive base) of the model to be set to an a priori value for all derived rules. The rules are obtained from an exemplar data set, and are derived by a technique called factoring, and this results in a table of rules called a ruling. These rules can then be used in pattern recognition applications. These techniques are shown be example as well as a more formal setting, and lastly these rules and ruling are likened to the structure both present and absent in the cerebellum.
机译：提出了一种表示从传感器，视频流图像，声音，文本等获得的数据的技术。该技术称为有限归纳序列（FI），被提议作为消除需要存储的数据的一种手段，而传统的数学模型并不能消除这些数据，而统计模型可以消除太多的数据。 FI是一个简单的想法，它基于符号推出技术，该技术允许将模型的阶数（归纳基数）设置为所有派生规则的先验值。这些规则是从示例性数据集中获得的，并且是通过称为分解的技术得出的，这产生了称为规则的规则表。这些规则然后可以在模式识别应用程序中使用。这些技术是作为示例以及更正式的设置显示的，最后，这些规则和裁定被比喻为小脑中存在和不存在的结构。

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