In this brief, the output reachable estimation and safety verification problems for multilayer
perceptron (MLP) neural networks are addressed. First, a conception called maximum …

The generalization error of deep neural networks via their classification margin is studied in
this paper. Our approach is based on the Jacobian matrix of a deep neural network and can …

Reliable and accurate short-term subway passenger flow prediction is important for
passengers, transit operators, and public agencies. Traditional studies focus on regular …

In this paper, we first define a measure of statistical sensitivity of a zero-order Takagi–
Sugeno (TS) fuzzy neural network (FNN) with respect to perturbation of weights and …

The vulnerability of artificial intelligence (AI) and machine learning (ML) against adversarial
disturbances and attacks significantly restricts their applicability in safety-critical systems …

This paper presents a flexible structure Radial Basis Function (RBF) neural network (FS-
RBFNN) and its application to water quality prediction. The FS-RBFNN can vary its structure …

A model-independent sensitivity analysis for (deep) neural network, Bilateral sensitivity
analysis (BiSA), is proposed to measure the relationship or dependency between neurons …

Neural networks provide a way to realize one of our human dreams to make machines think
like us. Artificial neural networks have been developed since Rosenblatt proposed the …

We propose a unified data modeling approach that is equally applicable to supervised
regression and classification applications, as well as to unsupervised probability density …

This study developed a methodology for formulating water level models to forecast river
stages during typhoons, comparing various models by using lazy and eager learning …