In recent years, the appearance of the broad learning system (BLS) is poised to
revolutionize conventional artificial intelligence methods. It represents a step toward building …

In big data fields, with increasing computing capability, artificial neural networks have shown
great strength in solving data classification and regression problems. The traditional training …

Continual learning (CL) aims to incrementally learn different tasks (such as classification) in
a non-stationary data stream without forgetting old ones. Most CL works focus on tackling …

Broad Learning System (BLS) that aims to offer an alternative way of learning in deep
structure is proposed in this paper. Deep structure and learning suffer from a time …

A novel neuro-fuzzy model named fuzzy broad learning system (BLS) is proposed by
merging the Takagi-Sugeno (TS) fuzzy system into BLS. The fuzzy BLS replaces the feature …

This paper contributes to the development of randomized methods for neural networks. The
proposed learner model is generated incrementally by stochastic configuration (SC) …

In the past decade, deep learning techniques have powered many aspects of our daily life,
and drawn ever-increasing research interests. However, conventional deep learning …

Recent developments in high-throughput technologies have accelerated the accumulation
of massive amounts of omics data from multiple sources: genome, epigenome …

In this paper, we propose a new method for hyperspectral images (HSI) classification,
aiming to take advantage of both manifold learning‐based feature extraction and neural …

The solution of instrumented indentation inverse problems by physically-based models still
represents a complex challenge yet to be solved in metallurgy and materials science. In …