The traditional decomposition–combination wind speed forecasting model has high complexity and a long calculation time. As a result, an ultra-short-term wind speed hybrid forecasting model based on a broad learning system (BLS) that combines improved variational mode decomposition (EPSO-VMD, EVMD) and subseries reconstruction (SR) is proposed in this work. The values of K and α in the EVMD are determined by minimum mean envelope entropy (MMEE) and enhanced particle swarm optimization (EPSO), and EVMD is used to decompose the original wind speed data. SR is applied to recombine the subseries obtained by EVMD to improve the forecasting efficiency. The sample entropy (SE) is used to quantify the subseries’ complexity, and they are then adaptively divided into high-entropy and low-entropy subseries. Adjacent high-entropy subseries of approximate entropy values are merged to obtain a new group of reconstructed high-entropy subseries, while the low-entropy subseries merge into a new subseries as well. Then, the forecasting results of the reconstructed high- and low-entropy subseries are calculated via the BLS and ARIMA models. Numerical simulation results show that the proposed method is more effective than traditional methods.

Purpose Gesture recognition based on biological signals played a crucial role in the field of human-computer interaction. Despite achieving decent results in improving gesture recognition accuracy using different algorithms, significant challenges remained in enhancing recognition accuracy for multimodal signals and special patients such as upper limb amputees. Methods This paper proposed a Broad Learning System (BLS) method to recognize hand movements. The input signals fused surface electromyographic (sEMG) and acceleration signals, and the fused data were mapped into contour maps to extract features, which were transformed into two-dimensional grayscale images to achieve efficient encoding. The paper utilized the public databases "Ninapro DB2 and DB7" to evaluate the performance of the proposed method. Results Experimental results showed that the average gesture recognition accuracy of BLS reached 97.73%, with an average testing time of 0.093s, outperforming the other two algorithms, K-Nearest Neighbor (0.497s) and Binary Tree (2.412s). Moreover, the fused signals exhibited higher recognition accuracy for amputees. Conclusion In conclusion, the accuracy and real-time of the method could satisfy the requirements for controlling the prosthetic. Hence, it provided a reliable approach for research in fields such as rehabilitative medicine and prosthetic control.