中文摘要：

提出了一种新的模糊竞争神经网络聚类模型NFCNNC，并将其应用到文本聚类中。NFCNNC将模糊中心聚类（FCC）算法得到的模糊聚类中心向量作为神经网络的权值，通过比较隶属度值得到获胜神经元。网络中仅两个神经元同时调节权值。隶属度值最大的神经元以较大的学习率调整权值，隶属度次大的神经元以较小的学习率调整权值，其他神经元权值不变。按照FCC算法调整模糊聚类中心向量值（即权值）和神经元的隶属度，当网络稳定时，即可确定聚类数。与传统模糊神经网络模型相比，本文的模糊神经网络模型具有结构简单、运行效率高、聚类精度高的优点，同时克服了传统算法需预先指定聚类数的局限性。通过对文本聚类的实验验证，本算法取得了良好的效果。

英文摘要：

This paper proposes a new fuzzy competitive neuron network clustering model, which is applied to the text clustering. NFCNNC uses the fuzzy central vectors acquired by the fuzzy central clustering （FCC） algorithm as the weights of the neuron network. The winner unit in the model is acqui~d by comparing the membership degree values between neurons. Only two neurons in the network are updated. The weights of the neuron with the largest membership degree values are updated by a larger learning rate,while the weights of the neuron with the second largest membership degree value are updated by a smaller learning rate, and the weights of other neurons are kept invariable. According to the formula of FCC algorithm, both the fuzzy center clustering vectors（weights of neuron network）and the membership degrees are adjusted, and the number of clustering is determined after the neuron network reaches stable. Compared with the traditional fuzzy neuron networks, the present model possesses the simpler structure, higher precision and the higher eficiency, and overcomes the defect of that the traditional algorithms need to know the number of clustering in advance. An example demonstrates the effectiveness of the present algorithm.