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首页> 外文期刊>Circuits and Systems I: Regular Papers, IEEE Transactions on >Time-Oriented Synthesis for a WTA Continuous-Time Neural Network Affected by Capacitive Cross-Coupling
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Time-Oriented Synthesis for a WTA Continuous-Time Neural Network Affected by Capacitive Cross-Coupling

机译：电容交叉耦合影响的WTA连续时间神经网络的时间定向综合

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摘要

A continuous time neural network built with nonlinear amplifiers which selects the largest item of a list $(WTA)$ is considered. The network receives and processes lists admitted one by one. If the processing and resetting times are imposed, our paper gives a method to find the circuit parameters assuring a correct operation. We take into account the capacitive coupling between input terminals and present complete existence and convergence results on the differential model. The main achievement consists of simple bounds for the processing and resetting times. They are inferred by an original method of decoupling the system model into solvable linear differential inequalities. Also, a new procedure to impose the stationary $WTA$ state is given. All these results are valid under various parameter restrictions. They lead to a neat design procedure which starts from imposed processing and resetting time and list density to determine the $WTA$ threshold, the interconnection conductance, the amplifier gain, the bias current. Numerical examples check and interpret the results.
机译：考虑了用非线性放大器构建的连续时间神经网络，该神经网络选择列表$（WTA）$中的最大项。网络接收并处理接受的列表。如果施加了处理和复位时间，我们的论文将提供一种方法来找到电路参数，以确保正确的操作。我们考虑了输入端子之间的电容耦合，并给出了差分模型的完整存在性和收敛性结果。主要成就包括处理和重置时间的简单范围。通过将系统模型解耦为可解线性微分不等式的原始方法可以推断出它们。另外，给出了施加固定$ WTA $状态的新过程。所有这些结果在各种参数限制下均有效。它们导致了一个整洁的设计过程，该过程从施加的处理和重置时间以及列表密度开始，以确定$ WTA $阈值，互连电导，放大器增益，偏置电流。数值示例检查并解释了结果。

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