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Dynamic projection network for associative memory and pattern classification and its utility for mechanical diagnosis.

机译：用于关联记忆和模式分类的动态投影网络及其在机械诊断中的实用性。

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This study proposed the utility of a type of dynamic networks known as a projection network in the realm of mechanical diagnosis. First, the projection network was studied for the relationship between its parameters and its properties including type, location and stability condition of equilibrium points and their interactions. A special case of stable axis equilibrium point or axis attractor was further studied and employed to formulate proper structure and parameter learning methods in each application area. The utility of the projection network was then established for associative memory and pattern classification.; In associative memory, guidelines and detailed algorithms for structure formulation and parameter learning were outlined. The application of projection network associative memory in mechanical diagnosis was then demonstrated for the diagnosis of a High Pressure Air Compressor (HPAC).; In pattern classification, this study developed and demonstrated the utility of the projection network for both supervised and unsupervised classification. First, it outlined the structure formulation and parameter learning for the projection network supervised classification system. It then incorporated the outlier elimination and clustering algorithm into the supervised classification system, thus making it capable of unsupervised classification tasks. The projection network supervised classification system was then evaluated and compared to the existing algorithms with three benchmark data including the Fisher's iris data, the heart disease data and the credit screening data. The projection network unsupervised classification system was also evaluated and compared to its supervised counterpart.; Finally this study demonstrated the utility of both the supervised and unsupervised classification system in mechanical diagnosis. First it demonstrated the utility of the supervised classification system in the diagnosis of two mechanical systems: a High Pressure Air Compressor and a jet engine. The utility of the unsupervised classification system was also demonstrated for the diagnosis of a High Pressure Air Compressor.
机译：这项研究提出了一种称为投影网络的动态网络在机械诊断领域中的实用性。首先，研究了投影网络的参数与属性之间的关系，包括平衡点的类型，位置和稳定性条件及其相互作用。进一步研究了稳定轴平衡点或轴吸引器的特殊情况，并在每个应用领域中采用了这种特殊情况来制定适当的结构和参数学习方法。然后建立了投影网络的效用，用于联想记忆和模式分类。在联想记忆中，概述了结构制定和参数学习的指南和详细算法。然后证明了投影网络联想记忆在机械诊断中的应用，用于诊断高压空气压缩机（HPAC）。在模式分类中，本研究开发并演示了投影网络在有监督和无监督分类中的实用性。首先，概述了投影网络监督分类系统的结构公式和参数学习。然后将离群值消除和聚类算法合并到监督分类系统中，从而使其能够执行无监督分类任务。然后，对投影网络监督分类系统进行评估，并将其与现有算法进行比较，并与包括费舍尔虹膜数据，心脏病数据和信用检查数据在内的三个基准数据进行比较。还评估了投影网络的无监督分类系统，并将其与有监督的对应系统进行比较。最后，本研究证明了有监督和无监督分类系统在机械诊断中的实用性。首先，它展示了监督分类系统在诊断两个机械系统中的实用性：高压空气压缩机和喷气发动机。还证实了无监督分类系统在诊断高压空气压缩机方面的实用性。

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