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 …

Neural networks, as powerful tools for data mining and knowledge engineering, can learn
from data to build feature‐based classifiers and nonlinear predictive models. Training neural …

Stochastic configuration network (SCN), as a novel incremental generation model with
supervisory mechanism, has an excellent superiority in solving large-scale data regression …

Graph Convolutional Networks (GCNs), as a prominent example of graph neural networks,
are receiving extensive attention for their powerful capability in learning node …

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

We present two effective methods for solving high-dimensional partial differential equations
(PDE) based on randomized neural networks. Motivated by the universal approximation …

The broad learning system (BLS) is an emerging approach for effective and efficient
modeling of complex systems. The inputs are transferred and placed in the feature nodes …

Incremental random weight networks (IRWNs) face the issues of weak generalization and
complicated network structure. There is an important reason: the learning parameters of …

Real-time predictive modelling with desired accuracy is highly expected in industrial artificial
intelligence (IAI), where neural networks play a key role. Neural networks in IAI require …

Neural networks have been widely used as predictive models to fit data distribution, and
they could be implemented through learning a collection of samples. In many applications …