Klopfian神经元模型在函数最小化中的应用

Proceedings of the International Workshop on Artificial Intelligence for Industrial Applications Pub Date : 1988-05-25 DOI:10.1109/AIIA.1988.13344

D. Politis

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引用次数: 0

摘要

作者讨论了A.G. Barto和R.S. Sutton(1981)基于Klopfian神经元模型开发的自适应学习控制器(ALC)算法用于函数最小化的使用。在这个应用中，ALC被直接放置在合成孔径雷达的信号处理环路中，分配给它的任务是最小化系统脉冲响应函数的3db宽度。这导致二次和可能的高阶系统相位误差的修正。

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Application of the Klopfian neuron model to function minimization

The author discusses the use of the adaptive learning controller (ALC) algorithms developed by A.G. Barto and R.S. Sutton (1981), based on the Klopfian neuron model, for function minimization. In this application the ALC is placed directly into the signal processing loop of a synthetic aperture radar and the task assigned to it is to minimize the 3-dB width of the system impulse response function. This results in the correction of the quadratic and possibly higher-order system phase errors.<>

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Proceedings of the International Workshop on Artificial Intelligence for Industrial Applications

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