中文摘要：

针对带有过程性模糊规则的时变信息处理问题，提出了一种模糊计算过程神经元网络。该模型将模糊神经网络推广到时域空间，可实现对过程性定量与定性混合信息的模糊计算。给出了一种5层结构的模糊计算过程神经元网络模型，并针对网络结构的优化问题给出了该网络模型的规则层节点的选取方法和相应的学习规则，对于具有较少输入节点的情况，网络有较快的训练速度。模糊过程神经元网络将传统模糊神经网络的模糊函数映像关系推广为模糊泛函映像，增强了对各种过程信息的综合处理能力。实际应用结果验证了模型和算法的有效性。

英文摘要：

Aiming at time-varying information process problems concerning process fuzzy rules, a fuzzy calculation process neural network model is presented. The model extends fuzzy neural network to time-domain space and can implement fuzzy calculation on process quantitative and qualitative mixed information. And a five-layer structure fuzzy calculation process neural network model is also given. According to the optimization problems of network structure, the selection methods and the corresponding learning rules on nile-layer nodes of the network model are put forward. For the situation that models contain just a few input nodes, the network has fast training speed. The fuzzy process neural network extends fuzzy function mapping relation of traditional fuzzy neural networks to mapping relation of fuzzy functional, which improves comprehensive information treatment capacity for various kinds of process information. Practical application results proved the effectiveness of the model and the algorithm.