Influence of human factors on risk associated with pressure equipment.

The analysis of previous research shows that indicators of human factors have not been sufficiently integrated into the models for risk assessment of pressure equipment to date. Therefore, the goal of this article is the creation of a universal measurement scale to assess the current condition of the impacts of human factors on the risk of pressure equipment exploitation in factories and plants. A research instrument with nine constructs and 61 dimensions was designed and tested on a sample size of 268 companies, by reliability, exploratory and confirmatory factor analysis.

Robust sequential learning of feedforward neural networks in the presence of heavy-tailed noise.

Feedforward neural networks (FFNN) are among the most used neural networks for modeling of various nonlinear problems in engineering. In sequential and especially real time processing all neural networks models fail when faced with outliers. Outliers are found across a wide range of engineering problems.

A growing and pruning sequential learning algorithm of hyper basis function neural network for function approximation.

Radial basis function (RBF) neural network is constructed of certain number of RBF neurons, and these networks are among the most used neural networks for modeling of various nonlinear problems in engineering. Conventional RBF neuron is usually based on Gaussian type of activation function with single width for each activation function. This feature restricts neuron performance for modeling the complex nonlinear problems.