Robust Incremental Broad Learning System for Data Streams of Uncertain Scale.

Due to its marvelous performance and remarkable scalability, a broad learning system (BLS) has aroused a wide range of attention. However, its incremental learning suffers from low accuracy and long training time, especially when dealing with unstable data streams, making it difficult to apply in real-world scenarios. To overcome these issues and enrich its relevant research, a robust incremental BLS (RI-BLS) is proposed. In this method, the proposed weight update strategy introduces two memory matrices to store the learned information, thus the computational procedure of ridge regression is decomposed, resulting in precomputed ridge regression. During incremental learning, RI-BLS updates two memory matrices and renews weights via precomputed ridge regression efficiently. In addition, this update strategy is theoretically analyzed in error, time complexity, and space complexity compared with existing incremental BLSs. Different from Greville's method used in the original incremental BLS, its results are closer to the solution of one-shot calculation. Compared with the existing incremental BLSs, the proposed method exhibits more stable time complexity and superior space complexity. The experiments prove that RI-BLS outperforms other incremental BLSs when handling both stable and unstable data streams. Furthermore, experiments demonstrate that the proposed weight update strategy applies to other random neural networks as well.

Size Effect of Platinum Nanoparticles over Platinum-Manganese Oxide on the Low-Temperature Oxidation of Toluene.

The effect of size of Pt nanoparticles has an important influence on the performance of supported Pt-based catalysts for the elimination of toluene. Herein, uniform Pt nanoparticles with average sizes of 1.5, 2.0, 2.5, 2.9, and 3.6 nm were obtained and supported on manganese oxide octahedral molecular sieves (OMS-2), and their catalytic performances for toluene oxidation were evaluated. Benefiting from the moderate interfacial interaction between nanoparticles and manganese oxide support, Pt/OMS-2-3 with the Pt particle size of 3.0 nm showed the best catalytic performance owing to the highest content of Pt2+ species. It also facilitates the formation of more abundant Mnδ+ (Mn2+ and Mn3+) and oxygen vacancies than that of the other sizes of the OMS-2-supported Pt nanoparticles, which can be filled by a large amount of adsorbed oxygen and converted into reactive oxygen species. We further showed that the resulting surface synergetic oxygen vacancies (Pt2+-Ov-Mnδ+) play a decisive part in catalyzing the complete oxidation of toluene. The result will provide new insights for designing efficient Pt-based catalysts for deep purification of toluene.