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DIGNET: a self-organizing neural network for automatic pattern recognition and classification

机译：DIGNET：用于自动模式识别和分类的自组织神经网络

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摘要

Abstract: The demonstrated ability of artificial neural networks to retrieve information that is addressed by content makes them a competitive candidate for automatic pattern recognition. Furthermore, their capability to reconstruct their memory from partially presented stored information compliments their recognition capabilities with classification. However, artificial neural networks (ANNs) are known to possess preferential behavior as far as the initial conditions and noise interference are concerned. A self-organizing artificial neural network is presented that exhibits deterministically reliable behavior to noise interference when the noise does not exceed a specified level of tolerance. The complexity of the proposed ANN, in terms of neuron requirements versus stored patterns, increases linearly with the number of stored patterns and their dimensionality. The self-organization of the proposed DIGNET is based on the idea of competitive generation and elimination of attraction wells in the pattern space. The same artificial neural network can be sued both for pattern recognition and classification.!
机译：摘要：证明的人工神经网络检索内容所寻址信息的能力使其成为自动模式识别的有力竞争者。此外，他们从部分显示的存储信息中重建内存的能力补充了其分类识别能力。然而，就初始条件和噪声干扰而言，人工神经网络（ANN）拥有优先的行为。提出了一种自组织的人工神经网络，当噪声不超过指定的容忍度时，该神经网络表现出对噪声干扰的确定性可靠的行为。就神经元需求相对于存储模式而言，所提出的人工神经网络的复杂性随存储模式的数量及其维数线性增加。所提出的DIGNET的自组织基于竞争性生成和消除模式空间中的吸引井的思想。可以使用相同的人工神经网络进行模式识别和分类。

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