Noisy time series prediction is a hot research topic in practical applications. Echo state
networks (ESNs) have superior performance on time series prediction. However, the ill …

Broad learning system (BLS) is a novel neural network with efficient learning and expansion
capacity, but it is sensitive to noise. Accordingly, the existing robust broad models try to …

Broad learning system (BLS) has attracted widespread attention owing to its concise
structure and efficient incremental learning based on ridge regression approximation of …

The Broad Learning System (BLS) has demonstrated strong performance across a variety of
problems. However, BLS based on the Minimum Mean Square Error (MMSE) criterion is …

Fine-grained visual classification (FGVC) is a challenging task due to its small inter-class
differences and large intra-class differences. Most existing methods rely on manual labeling …

As an emerging learning method belonging to the family of neural networks, the broad
learning system (BLS) has been recently proved to be effective and efficient to perform …

The broad learning system (BLS) is an effective machine learning model that exhibits
excellent feature extraction ability and fast training speed. However, the traditional BLS is …

The sensitivity of the broad learning system (BLS) based on the minimum-mean-square
error (MMSE) criterion to non-Gaussian noise limits the application of this system. In order to …

Broad learning systems (BLSs) are becoming increasingly popular due to their fast and
superior learning capabilities. However, their performances are susceptible to outliers and …

Artificial intelligence generated image content generates desired image content according to
given instructions. It is widely applied in digital marketing, video game design, and movie …