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    • 1. 发明授权
    • 神经网络的“晴雨表”神经元
    • US5535303A
    • 1996-07-09
    • US202708
    • 1994-02-22
    • Leon K. EkchianDavid D. JohnsonWilliam F. Smith
    • Leon K. EkchianDavid D. JohnsonWilliam F. Smith
    • G06N3/063G06G7/12
    • G06N3/0635
    • A "Barometer" Neuron enhances stability in a Neural Network System that, when used as a track-while-scan system, assigns sensor plots to predicted track positions in a plot/track association situation. The "Barometer" Neuron functions as a bench-mark or reference system node that equates a superimposed plot and track to a zero distance as a "perfect" pairing of plot and track which has a measured/desired level of inhibition. The "Barometer" Neuron responds to the System inputs, compares these inputs against the level of inhibition of the "perfect" pair, and generates a supplied excitation or inhibition output signal to the System which adjusts the System to a desired value at or near 1.0; this the reference level of inhibition of the "perfect" pair.
    • “晴雨表”神经元增强了在神经网络系统中的稳定性,当用作轨道同时扫描系统时,可以在情节/轨迹关联情况下将传感器图分配给预测的轨迹位置。 “晴雨表”神经元用作基准或参考系统节点,将叠加图和轨迹等于零距离,作为曲线和轨迹的“完美”配对,其具有测量/期望的抑制水平。 “晴雨表”神经元响应系统输入,将这些输入与“完美”对的抑制电平进行比较,并产生提供的激励或抑制输出信号给系统,该系统将系统调整到或接近1.0 ; 这是对“完美”对的抑制的参考水平。
    • 2. 发明授权
    • Vector neural networks
    • 矢量神经网络
    • US5432889A
    • 1995-07-11
    • US983374
    • 1992-11-30
    • Leon K. EkchianDavid D. JohnsonWilliam F. Smith
    • Leon K. EkchianDavid D. JohnsonWilliam F. Smith
    • G01S7/41G01S13/72G06K9/32G06N3/04G06T7/20G06K9/62
    • G06T7/20G01S13/726G01S7/417G06K9/3241G06N3/04Y10S348/909
    • A vector neural network (VNN) of interconnected neurons is provided in transition mappings of potential targets wherein the threshold (energy) of a single frame does not provide adequate information (energy) to declare a target position. The VNN enhances the signal-to-noise ratio (SNR) by integrating target energy over multiple frames including the steps of postulating massive numbers of target tracks (the hypotheses), propagating these target tracks over multiple frames, and accommodating different velocity targets by pixel quantization. The VNN then defers thresholding to subsequent target stages when higher SNR's are prevalent so that the loss of target information is minimized, and the VNN can declare both target location and velocity. The VNN can further include target maneuver detection by a process of energy balancing hypotheses.
    • 在潜在目标的过渡映射中提供互连神经元的向量神经网络(VNN),其中单个帧的阈值(能量)不能提供足够的信息(能量)来声明目标位置。 VNN通过在多个帧上集成目标能量来增强信噪比(SNR),包括假设大量目标轨迹(假设),在多个帧上传播这些目标轨迹,以及通过像素适应不同的速度目标 量化。 当较高的信噪比是普遍的时候,VNN就会延迟到后续的目标阶段,从而使目标信息的丢失最小化,并且VNN可以声明目标位置和速度。 VNN还可以通过能量平衡假设过程进一步包括目标机动检测。