Is Extreme Learning Machine Feasible? A Theoretical Assessment (Part II)

Extreme Learning Machine: A Review

Feedforward neural networks (FFNN) have been utilised for various research in machine learning and they have gained a significantly wide acceptance. However, it was recently noted that the feedforward neural network has been functioning slower than needed. As a result, it has created critical bottlenecks among its applications. Extreme Learning Machines (ELM) were suggested as alternative learn...

Extreme Learning Machine

Slow speed of feedforward neural networks has been hampering their growth for past decades. Unlike traditional algorithms extreme learning machine (ELM) [5][6] for single hidden layer feedforward network (SLFN) chooses input weight and hidden biases randomly and determines the output weight through linear algebraic manipulations. We propose ELM as an auto associative neural network (AANN) and i...

Contour Crafting Process Plan Optimization Part II: Multi–Machine Cases

Contour Crafting is an emerging technology that uses robotics to construct free form building structures by repeatedly laying down layers of material such as concrete. The Contour Crafting technology scales up automated additive fabrication from building small industrial parts to constructing buildings. Tool path planning and optimization for Contour Crafting benefit the technology by increasin...

A Learning Machine: Part I

Machines would be more useful if they could learn to perform tasks for which they were not given precise methods. Difficulties that attend giving a machine this ability are discussed. It i s proposed that the program of a stored-program computer be gradually improved by a learning procedure which tries many programs and chooses, from the instructions that may occupy a given location, the one mo...

A Robust Online Sequential Extreme Learning Machine