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    • 3. 发明申请
    • NONLINEAR IDENTIFICATION USING COMPRESSED SENSING AND MINIMAL SYSTEM SAMPLING
    • 使用压缩感知和最小系统采样的非线性识别
    • US20110270590A1
    • 2011-11-03
    • US12776180
    • 2010-05-07
    • Vladimir AparinRobert P. Gilmore
    • Vladimir AparinRobert P. Gilmore
    • G06F17/10
    • H03M7/30G06K9/00496H03F1/3258
    • Compressed sensing is used to determine a model of a nonlinear system. In one example, L1-norm minimization is used to fit a generic model function to a set of samples thereby obtaining a fitted model. Convex optimization can be used to determine model coefficients that minimize the L1-norm. In one application, the fitted model is used to calibrate a predistorter. In another application, the fitted model function is used to predict future actions of the system. The generic model is made of up of constituent functions that may or may not be orthogonal to one another. In one example, an initial model function of non-orthogonal constituent functions is orthogonalized to generate a generic model function of constituent orthogonal functions. Although the number of samples to which the generic model is fitted can be less than the number of model coefficients, the fitted model nevertheless accurately models system nonlinearities.
    • 压缩感测用于确定非线性系统的模型。 在一个示例中,L1范数最小化用于将通用模型函数拟合到一组样本,从而获得拟合模型。 可以使用凸优化来确定最小化L1范数的模型系数。 在一个应用中,拟合模型用于校准预失真器。 在另一个应用中,拟合模型函数用于预测系统的未来动作。 通用模型由可能彼此或可能不相互正交的组成函数构成。 在一个示例中,非正交组成函数的初始模型函数被正交化以生成组成正交函数的通用模型函数。 虽然通用模型拟合的样本数量可以小于模型系数的数量,但拟合模型仍然能够准确地模拟系统非线性。
    • 6. 发明申请
    • METHOD AND APPARATUS FOR UNSUPERVISED TRAINING OF INPUT SYNAPSES OF PRIMARY VISUAL CORTEX SIMPLE CELLS AND OTHER NEURAL CIRCUITS
    • 主要视觉CORTEX简单细胞和其他神经电路输入信号的不间断训练的方法和装置
    • US20120303566A1
    • 2012-11-29
    • US13115154
    • 2011-05-25
    • Vladimir Aparin
    • Vladimir Aparin
    • G06N3/08G06N3/063
    • G06N3/063
    • Certain aspects of the present disclosure present a technique for unsupervised training of input synapses of primary visual cortex (V1) simple cells and other neural circuits. The proposed unsupervised training method utilizes simple neuron models for both Retinal Ganglion Cell (RGC) and V1 layers. The model simply adds the weighted inputs of each cell, wherein the inputs can have positive or negative values. The resulting weighted sums of inputs represent activations that can also be positive or negative. In an aspect of the present disclosure, the weights of each V1 cell can be adjusted depending on a sign of corresponding RGC output and a sign of activation of that V1 cell in the direction of increasing the absolute value of the activation. The RGC-to-V1 weights can be positive and negative for modeling ON and OFF RGCs, respectively.
    • 本公开的某些方面提供了用于无监督训练初级视皮层(V1)简单细胞和其他神经电路的输入突触的技术。 提出的无监督训练方法使用简单的神经元模型用于视网膜神经节细胞(RGC)和V1层。 该模型简单地添加每个单元的加权输入,其中输入可以具有正值或负值。 所得的加权输入总和代表也可以是正或负的激活。 在本公开的一方面,可以根据对应的RGC输出的符号和在增加激活的绝对值的方向激活该V1小区的符号来调整每个V1小区的权重。 RGC-to-V1权重可以分别为ON和OFF RGC的正负值。
    • 9. 发明申请
    • COMMUNICATION AND SYNAPSE TRAINING METHOD AND HARDWARE FOR BIOLOGICALLY INSPIRED NETWORKS
    • 通信和仿真培训方法和生物信息网络的硬件
    • US20120011088A1
    • 2012-01-12
    • US12831540
    • 2010-07-07
    • Vladimir AparinYi Tang
    • Vladimir AparinYi Tang
    • G06N3/063G06N3/08
    • G06N3/0635G06N3/02G06N3/06G06N3/08G06N99/005
    • Certain embodiments of the present disclosure support techniques for training of synapses in biologically inspired networks. Only one device based on a memristor can be used as a synaptic connection between a pair of neurons. The training of synaptic weights can be achieved with a low current consumption. A proposed synapse training circuit may be shared by a plurality of incoming/outgoing connections, while only one digitally implemented pulse-width modulation (PWM) generator can be utilized per neuron circuit for generating synapse-training pulses. Only up to three phases of a slow clock can be used for both the neuron-to-neuron communications and synapse training. Some special control signals can be also generated for setting up synapse training events. By means of these signals, the synapse training circuit can be in a high-impedance state outside the training events, thus the synaptic resistance (i.e., the synaptic weight) is not affected outside the training process.
    • 本公开的某些实施例支持在生物启发网络中训练突触的技术。 只有一个基于忆阻器的设备可以用作一对神经元之间的突触连接。 突触体重的训练可以用低电流消耗来实现。 所提出的突触训练电路可以由多个输入/输出连接共享,而每个神经元电路只能使用一个数字实现的脉宽调制(PWM)发生器来产生突触训练脉冲。 只有三个阶段的慢时钟可以用于神经元到神经元通信和突触训练。 还可以生成一些特殊的控制信号来设置突触训练事件。 通过这些信号,突触训练电路可以在训练事件之外处于高阻抗状态,因此在训练过程之外不会影响突触电阻(即突触重量)。
    • 10. 发明申请
    • METHOD AND APPARATUS FOR USING PRE-DISTORTION AND FEEDBACK TO MITIGATE NONLINEARITY OF CIRCUITS
    • 使用预失真和反馈来减轻电路非线性的方法和装置
    • US20100323641A1
    • 2010-12-23
    • US12489380
    • 2009-06-22
    • Vladimir AparinGary John Ballantyne
    • Vladimir AparinGary John Ballantyne
    • H04B1/04
    • H03F1/34H03F1/3258
    • Techniques for mitigating nonlinearity of circuits with both pre-distortion and feedback are described. An apparatus may include at least one circuit (e.g., an upconverter, a power amplifier, etc.), a pre-distortion circuit, and a feedback circuit. The circuit(s) may generate an output signal having distortion components due to their nonlinearity. The pre-distortion circuit may receive an input signal and generate a pre-distorted signal based on at least one coefficient determined by the nonlinearity of the circuit(s). The pre-distortion circuit may adaptively determine the coefficient(s) based on the input signal and an error signal. The feedback circuit may generate the error signal based on the input signal and the output signal and may filter the error signal to obtain a filtered error signal. The circuit(s) may process the pre-distorted signal and the filtered error signal to generate the output signal, which may have attenuated distortion components due to pre-distortion and feedback.
    • 描述了减轻预失真和反馈的电路非线性的技术。 装置可以包括至少一个电路(例如,上变频器,功率放大器等),预失真电路和反馈电路。 电路可以由于它们的非线性而产生具有失真分量的输出信号。 预失真电路可以接收输入信号,并且基于由电路的非线性确定的至少一个系数产生预失真信号。 预失真电路可以基于输入信号和误差信号自适应地确定系数。 反馈电路可以基于输入信号和输出信号产生误差信号,并且可以对误差信号进行滤波以获得滤波后的误差信号。 电路可以处理预失真信号和滤波后的误差信号以产生输出信号,其可能由于预失真和反馈而具有衰减的失真分量。