用于模式识别和计算的多级振荡神经网络模型
A Model of an Oscillatory Neural Network with Multilevel Neurons for Pattern Recognition and Computing
摘要：
本研究使用了一种新的方法，多级神经元和高阶同步效应所描述的一个家庭的特殊度量，模式识别的振荡神经网络（ONN）。该网络的输出振荡器（神经元）在其与参考振荡器的同步值中具有多级变化，并且允许将输入模式分类为一组类别。ONN模型是在热耦合二氧化钒振荡器上实现的。采用模拟退火算法对ONN进行训练，以选择网络参数。结果表明，ONN是能够分类512视觉模式（作为一个单元阵列3 * 3，通过对称性分布成102类）到一组类的最大数量的元素多达14。网络的分类能力取决于内部噪声水平和同步有效性参数。该模型允许设计具有高净数据吞吐量的神经网络的多级输出级联。所提出的方法可以应用于各种耦合机制和振荡器拓扑结构的光网络。
英文摘要：
The current study uses a novel method of multilevel neurons and high order synchronization effects described by a family of special metrics, for pattern recognition in an oscillatory neural network (ONN). The output oscillator (neuron) of the network has multilevel variations in its synchronization value with the reference oscillator, and allows classification of an input pattern into a set of classes. The ONN model is implemented on thermally-coupled vanadium dioxide oscillators. The ONN is trained by the simulated annealing algorithm for selection of the network parameters. The results demonstrate that ONN is capable of classifying 512 visual patterns (as a cell array 3 * 3, distributed by symmetry into 102 classes) into a set of classes with a maximum number of elements up to fourteen. The classification capability of the network depends on the interior noise level and synchronization effectiveness parameter. The model allows for designing multilevel output cascades of neural networks with high net data throughput. The presented method can be applied in ONNs with various coupling mechanisms and oscillator topology.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
