When the fine-grained recognition problems of image classification processed, broad learning system (BLS) is more efficient in classification, but has difficulty in distinguishing features with large similarities. Sparse representation classification (SRC) is more capable of handling similarity features, but is more computationally expensive. To better use the BLS model to tackle the fine-grained recognition problem and improve the ability to handle similarity features, this article combines the advantages of BLS and SRC, and proposes a broad sparse fine-grained image classification model based on dictionary selection strategy, dictionary broad sparse representation classification (DBSRC). First, to solve the parameter selection problem of the BLS model, leave one out cross validation (LOO) is introduced to quickly find the better regularization parameters and build a BLS-LOO coarse-grained classification model. Then propose reliability criteria based on a threshold selection strategy for the selection of fine-grained images. Next, an adaptive dictionary selection strategy is designed based on the output of the BLS-LOO to construct a sparse subdictionary for each fine-grained image that is not distinguished by the BLS-LOO. Finally, a sparse subdictionary based SRC model is used to classify fine-grained images. Experimental results show that DBSRC achieves good classification performance on three image datasets with different complex dimensions, ImageNet, USPS, and Pavia, and has strong processing capability for fine-grained features.

The bearing system of an alternating current (AC) motor is a nonlinear dynamics system. The working state of rolling bearings directly determines whether the machine is in reliable operation. Therefore, it is very meaningful to study the fault diagnosis and prediction of rolling bearings. In this paper, a new fault diagnosis method based on variational mode decomposition (VMD), Hilbert transform (HT), and broad learning model (BLM), called VHBLFD is proposed for rolling bearings. In the VHBLFD method, the VMD is used to decompose the vibration signals to obtain intrinsic mode functions (IMFs). The HT is used to process the IMFs to obtain Hilbert envelope spectra, which are transformed into the mapped features and the enhancement nodes of BLM according to the complexity of the modeling tasks, and the nonlinear transformation mean according to the characteristics of input data. The BLM is used to classify faults of the rolling bearings of the AC motor. Next, the pseudo-inverse operation is used to obtain the fault diagnosis results. Finally, the VHBLFD is validated by actual vibration data. The experiment results show that the BLM can quickly and accurately be trained. The VHBLFD method can achieve higher identification accuracy for multi-states of rolling bearings and takes on fast operation speed and strong generalization ability.