中文摘要：

针对全连接前馈神经网络不能有效应对时变系统的问题，提出一种动态自适应模块化神经网络结构．该网络采用减法聚类算法在线辨识工况数据的空间分布，利用RBF神经元实现对数据样本空间的划分，并结合模糊策略将不同子样本空间的数据动态分配给不同的子网络，最后对各子网络的输出进行集成．该模块化网络中子网络数量和子网络规模都能根据所学时变任务动态自适应调整．通过对不同时变系统的预测表明了该网络能够有效跟踪时变系统．

英文摘要：

Due to the fact that the fully coupled feedforward neural network can not effectively deal with the problem of time-varying systems, a dynamic adaptive modular neural network model is proposed. In this model, the substractive cluster algorithm is applied to online identification of the spatial distribution of the condition data. RBF neurons are used to decompose the learning sample space and combined with fuzzy strategy to dynamically allocate different sub-sample space learning data to different sub-networks. Finally, the output of the modular neural network can be achieved by integrating the output of the sub-networks. The number of the sub-networks and the architecture of the subnet-works can be adaptively adjusted based on the current learning time-varying task. Experiment results on different time-varying systems show that proposed model can effectively tracke the time-varying system.