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Competitive Dynamics and Pattern Formation in a Large Array of Opto-electronic Feedback Circuit System

机译:大型光电反馈电路系统中的竞争动态和模式形成

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In artificial neural networks (ANN) individual nodes are used as processing units that perform simple computations. The computations can be performed based on unsupervised or supervised learning schemes. One type of learning scheme is a competitive unsupervised approach. In the competitive approach different nodes compete to become the "winner(s)", representing the highest activity level. In a large array opto-electronic system the competitive dynamics is not restricted to single elements but is some specially distributed structure. This interaction is typical for partially distributed nonlinear systems with complex behavior but may be unusual behavior in other systems with large arrays of elements for example some ANN. With an opto-electronic system it may be possible to consider new dynamics and more complex behavior for systems with large arrays. A different approach for parallel high resolution information processing that potentially goes beyond processing large numbers of neurons or elements is considered. NN has been successful in processing low-resolution images. Hopefully opto-electronic systems can generate similar mechanisms seen in NN such as cooperation and competition. Perhaps different self-organizing structures or patterns generated by these systems have some features similar to competition and cooperation. These types of structure or pattern interactions can be possible building blocks for more robust computational processes.
机译:在人工神经网络中(ANN)各个节点用作执行简单计算的处理单元。可以基于无监督或监督的学习方案执行计算。一种学习方案是一种竞争无人监督的方法。在竞争方法中,不同的节点竞争成为“获胜者”,代表最高活动水平。在一个大的阵列光电子系统中,竞争动态不限于单个元素,而是一些特殊的分布式结构。这种相互作用对于具有复杂行为的部分分布式非线性系统,但在其他系统中可能是具有大的元素阵列的其他系统的不寻常行为。通过光电系统,可以考虑具有大阵列的系统的新动态和更复杂的行为。考虑了不同的平行高分辨率信息处理的不同方法,其可能超越处理大量神经元或元件。 NN已经成功地处理了低分辨率图像。希望光电系统可以在NN中产生类似的机制,如合作和竞争。也许这些系统产生的不同的自组织结构或模式具有类似于竞争和合作的一些功能。这些类型的结构或模式交互可以是用于更强大的计算过程的构建块。

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