Abstract: In response to the complexity and non-linear characteristics of chaotic time series,this paper proposes a novel neural network model specifically designed to address these challenges:the attention-enhanced dynamic self-organizing modular neural network(ADAMNN).Grounded in the divide-and-conquer philosophy,this model employs an attention mechanism to compute the similarity between different sub-networks and input data,facilitating an adaptive partitioning of sub-networks through hierarchical clustering.Subsequently,a dynamic growth mechanism,based on hierarchical clustering,adjusts the size of sub-network clusters.Ultimately,activated sub-network clusters are employed for online learning of input samples.Simultaneously,we introduce a novel attention-based sub-network weighted ensemble output method,integrating traditional ensemble output approaches.Ultimately,experiments were conducted on the Mackey-Glass time series,the rapidly varying MG time series,in the realm of nonli-near system identification,and using gas concentration datasets from coal mining operations.The ADAMNN model exhibited its proficiency in real-time updates of sub-network centers and the dynamic formation of sub-network clusters.Moreover,compared to dynamic self-organizing modular neural networks based on Euclidean space,ADAMNN exhibits an approximately 40% improvement in prediction accuracy.

Key words: Modular neural networks, Self-organization neural networks, Chaotic time series, Attention mechanism, Hierarchical clustering