The single-layer perceptron, introduced by Rosenblatt in 1958, is one of the earliest and
simplest neural network models. However, it is incapable of classifying linearly inseparable …

Recently, robotic manipulators have been playing an increasingly critical part in scientific
research and industrial applications. However, modeling of robotic manipulators is …

Genetic Algorithms (GA) as a tool for a search and optimizing methodology has now
reached a mature stage. It has found many useful applications in both the scientific and …

Conventional model-based data processing methods are computationally expensive and
require experts' knowledge for the modelling of a system. Neural networks are a model-free …

A method defining a reject option that is applicable to a given 0-reject classifier is proposed.
The reject option is based on an estimate of the classification reliability, measured by a …

In some practical applications of neural networks, fast response to external events within an
extremely short time is highly demanded and expected. However, the extensively used …

In this research, we explore the structural damage detection using frequency response
functions (FRFs) as input data to the back-propagation neural network (BPNN). Such …

We survey research of recent years on the supervised training of feedforward neural
networks. The goal is to expose how the networks work, how to engineer them so they can …

The practical application of genetic algorithms (GA) to the solution of engineering problems
is a rapidly emerging approach in the field of control engineering and signal processing …

This article focuses on gradient-based backpropagation algorithms that use either a
common adaptive learning rate for all weights or an individual adaptive learning rate for …