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Self-organizing fuzzy clustering neural network and application to electronic countermeasures effectiveness evaluation

机译：自组织模糊聚类神经网络及其在电子对抗效能评估中的应用

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摘要

A self-organizing fuzzy clustering neural network by combining the self-organizing Kohonen clustering network with the fuzzy theory is proposed. This network model is designed for the effectiveness evaluation of electronic countermeasures, which not only exerts the advantages of the fuzzy theory, but also has a good ability in machine learning and data analysis. The subjective value of sample versus class is computed by the fuzzy computing theory, and the classified results obtained by self-organizing learning of Kohonen neural network are represented on output layer. Meanwhile, the fuzzy competition learning algorithm keeps the similar information between samples and overcomes the disadvantages of neural network which has fewer samples. The simulation result indicates that the proposed algorithm is feasible and effective.
机译：提出了一种将自组织Kohonen聚类网络与模糊理论相结合的自组织模糊聚类神经网络。该网络模型是为电子对策的有效性评估而设计的，它不仅发挥了模糊理论的优势，而且还具有良好的机器学习和数据分析能力。通过模糊计算理论计算出样本与类别的主观价值，并在输出层上表示通过对Kohonen神经网络的自组织学习获得的分类结果。同时，模糊竞争学习算法保持样本之间的相似信息，克服了样本较少的神经网络的弊端。仿真结果表明该算法是可行和有效的。

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