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.