Over the past few years, neural networks have exhibited remarkable results for various
applications in machine learning and computer vision. Weight initialization is a significant …

This chapter contains a description of the historical evolution of artificial neural networks
since their inception, with the appearance of the first relevant learning method by Paul …

Data mining techniques are capable of extracting valuable knowledge from large and
variable databases. This work proposes a data mining method for municipal financial …

This paper presents a new vehicle classification and develops a traffic monitoring detector to
provide reliable vehicle classification to aid traffic management systems. The basic principle …

Initial weight choice has been recognized to be an important aspect of the training
methodology for sigmoidal feedforward neural networks. In this paper, a new mechanism for …

The noise associated with conventional techniques for evolutionary improvement of neural
network architectures is reduced so that of an optimum architecture can be determined more …

This paper reviews the combination of Artificial Neural Networks (ANN) and Evolutionary
Optimisation (EO) to solve challenging problems for the academia and the industry. Both …

The choice of the initial values of neural network parameters is of crucial importance for the
conduct and successful completion of training. The most known and most used method to …

Evolving connectionist systems: Characterisation, simplification, formalisation, explanation
and optimisation - University of Otago Logo image Menu Outputs Thesis Deposit Guide FAQs …

Multilayer neural networks are trained to solve optimization problems. Genetic algorithms
are adopted to" evolve" weights, unveiling new points in the definition domain. As the …