苏理云，熊浪，杨佳玲发表在《Applied Soft Computing》上的论文获得2023年川渝科技学术大会优秀论文三等奖！
Su Liyun, Xiong L.，Yang J., Multi-Attn BLS: Multi-head attention mechanism with broad learning system for chaotic time series prediction, Applied Soft Computing, 2023. https://www.sciencedirect.com/science/article/abs/pii/S1568494622008808
按照《2023川渝科技学术大会方案》和《重庆市科学技术协会关于开展2023川渝科技学术大会优秀论文评选工作的通知》有关要求，经自愿申报，资格审查，专家评审等环节，并经市科协党组会研究，重庆市拟产生2023川渝科技学术论文特等奖2篇，一等奖10篇，二等奖30篇，三等奖50篇。现将拟获奖名单予以公示。
http://www.cqast.cn/htm/2023-11/21/content_52064101.htm
论文摘要：The observational 1-D signals available for realizing the highly accurate intrinsic attractor fitting of deep learning network approaches are often insufficient because of the complexity and nonlinearity of chaotic time series. Unlike deep models, a broad learning system (BLS) with the attention mechanism exhibits a unique and preeminent pattern prediction ability. Thus, this system has been applied as a practical trend in many fields. However, the application of multi-head attention fused manifold broad learning architecture to chaotic time series prediction remains inadequate. Thus, a multi-head attentional BLS (Multi-Attn BLS) for chaotic time series prediction is proposed in this study to improve the prediction accuracy of chaotic time series further. Our model develops a novel framework that combines the high computational efficiency of broad learning with the multi-head attention mechanism. First, the received data are reconstructed into fixed-size tuples. The multidimensional arrays with embedding dimensions and time delay are used as the input to a broad learning network. Subsequently, a robust BLS with a spatiotemporal multi-head attention mechanism is developed to depict the internal dynamic evolution. The Multi-Attn BLS model can capture key spatiotemporal feature information and achieve high predictive performance. It also has a good generalization ability in practical nonlinear complex systems. Comparative experiments with the traditional long short-term memory (LSTM) network and the primitive BLS show that its computing speed and generalization ability are improved. Furthermore, the network is good at capturing the spatiotemporal features of the sequence because of the multi-head attention mechanism. The experimental results show that our model outperforms BLS, ridge regression, and LSTM on the four main evaluation indicators (root mean square error, root mean square percentage error, mean absolute error, and mean absolute percentage error) in predicting classical systems (Lorenz and Rossler systems). Moreover, the model has an excellent prediction effect in the real-world chaotic system of sea clutter.
Applied Soft Computing：SCI 一区，影响因子8.7.
Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems. Soft computing is a collection of methodologies, which aim to exploit tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities.
