This paper proposes a constructive approach for finding arbitrary (real or complex) roots of
arbitrary (real or complex) polynomials by multilayer perceptron network (MLPN) using …

This paper proposes new modified constrained learning neural root finders (NRFs) of
polynomial constructed by backpropagation network (BPN). The technique is based on the …

This letter proposes a novel neural root finder based on the root moment method (RMM) to
find the arbitrary roots (including complex ones) of arbitrary polynomials. This neural root …

By using the viewpoint of modern computational algebraic geometry, we explore properties
of the optimization landscapes of deep linear neural network models. After providing …

Over the years, neural networks have shown its strength in various fields of research. There
is a vast improvement in the efficiency and effectiveness of various classification techniques …

This study examines a variety of artificial neural network (ANN) models in terms of their
classification efficiency in an orthopedic disease, namely osteoporosis. Osteoporosis risk …

Higher Order Neural Networks (HONN) are characterized with fast learning abilities,
stronger approximation, greater storage capacity, higher fault tolerance capability and …

Parameterized systems of polynomial equations arise in many applications in science and
engineering with the real solutions describing, for example, equilibria of a dynamical system …

In finding roots of polynomials, often two or more roots that are close together in solution
space are very difficult to be resolved by a root-finder. To solve this problem, this Letter …

Over the last few decades, application of higher order neural networks (HONNs) to time
series forecasting have shown some promise compared to statistical approaches and …