Background The response of many biomedical systems can be modelled using a linear
combination of damped exponential functions. The approximation parameters, based on …

In spite of the prominence of extreme learning machine model, as well as its excellent
features such as insignificant intervention for learning and model tuning, the simplicity of …

For the purpose of learning on graphs, we hunt for a graph feature representation that
exhibit certain uniqueness, stability and sparsity properties while also being amenable to …

Extreme learning machine (ELM) is a learning algorithm for single-hidden layer feedforward
neural networks (SLFNs) which randomly chooses hidden nodes and analytically …

In this work, we develop a pseudoinverse-based static finite-element solver to model the
elastic deformation and non-local brittle fracture of solids. The pseudoinverse of the finite …

In this work, a novel supervised learning method, the Minimal Learning Machine (MLM), is
proposed. Learning in MLM consists in building a linear mapping between input and output …

The emergence of smart grids has posed great challenges to traditional power system
control given the multitude of new risk factors. This paper proposes an online supplementary …

For pin-jointed assemblies with many members or self-stress states, the form-finding
problem using conventional methods generally involves considerable computational …

A new deployable truss structure for large caliber antennas was presented in this work. The
deploying process of the deployable structure was studied using the Moore–Penrose …

The computation of the Moore–Penrose inverse of structured strictly totally positive matrices
is addressed. Since these matrices are usually very ill-conditioned, standard algorithms fail …