首页> 外国专利> RADAR WITH FREQUENCY RETUNING BY APERTURE INVERSE SYNTHESISING AND TWO-LEVEL NEURON-NETWORK IDENTIFICATION OF OBJECTS BY COMBINATION OF ATTRIBUTES

RADAR WITH FREQUENCY RETUNING BY APERTURE INVERSE SYNTHESISING AND TWO-LEVEL NEURON-NETWORK IDENTIFICATION OF OBJECTS BY COMBINATION OF ATTRIBUTES

机译:孔逆合成消除频率的雷达及属性组合的两级神经网络识别

摘要

FIELD: physics.;SUBSTANCE: proposed method consists in using known devices, an altitude determination unit, a unit for isolation effective are of dissipation and a unit for isolation flying object full speed, with appropriate modification of communication between units. Neuron-network classifiers used in the radar two-level identifier are synthesized by principle of transposition of sub networks into complex artificial neuron network. Proposed approach allows improving ability of each sub network to identify particular attribute. Particular solutions in output layers of neurons are integrated into general solution by special rules corresponding to the structure of complex neuron network.;EFFECT: higher validity of flying objects identification brought about be expanded combination of attributed in neuron network classifier and of two-level system.;1 dwg
机译:领域:物理;实质:所提出的方法包括使用已知的装置,高度确定单元,用于隔离有效耗散的单元和用于隔离飞行物体全速的单元,并适当地修改单元之间的通信。雷达二级识别器中使用的神经元网络分类器是通过将子网络转换为复杂的人工神经元网络的原理来合成的。所提出的方法允许提高每个子网识别特定属性的能力。通过对应于复杂神经元网络结构的特殊规则,将神经元输出层中的特定解集成到一般解中。效果:飞行物识别的更高有效性带来了神经元网络分类器和两级系统属性的扩展组合。; 1 dwg

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