Purpose This paper aims to use different machine learning (ML) algorithms for the prediction of inverse kinematic solutions in parallel manipulators (PMs) to overcome the computational difficulties and approximations involved with the analytical methods. The results obtained from the ML...

Purpose This paper aims to illustrate the growing role of machine learning techniques in robotics. Design/methodology/approach Following an introduction which includes a brief historical perspective, this paper provides a short introduction to machine learning techniques. It then provides...

Purpose The study proposed a human–robot interaction (HRI) framework to enable operators to communicate remotely with robots in a simple and intuitive way. The study focused on the situation when operators with no programming skills have to accomplish teleoperated tasks dealing with randomly...

Purpose Most of the redundant dual-arm robots are singular free, dexterous and collision free compared to other robotic arms. This paper aims to analyse the workspace of redundant arms to study the manipulability. Furthermore, multi-layer perceptron (MLP) algorithm is used to determine the...

We explore convenient analytic properties of distributions constructed as mixtures of scaled and shifted t-distributions. A feature that makes this family particularly desirable for econometric applications is that it possesses closed-form expressions for its anti-derivatives (e.g., the...

Predicting corporate distress can have a significant impact on the economy because it serves as an efficient early warning signal. This study develops distress prediction models incorporating both governance and financial variables and examines the impact of major corporate governance...

Burn‐in is an engineering method extensively used to screen out infant mortality failure defects. Previous studies have attempted to determine the optimum burn‐in time and cost for a device or a system. However, for the mathematical model, many assumptions are inappropriate due to practical...

Control chart pattern recognition is a critical issue in statistical process control, as unnatural patterns on control charts are often associated with specific assignable causes adversely affecting the process. Several researchers have recently applied neural networks to pattern recognition for...

This study presents an effective means of applying neural networks to achieve robust design with dynamic characteristic considerations. Two neural networks are constructed to train the data set in the Taguchi’s orthogonal array (OA): one to search for the optimal condition, and the other to...