The cascade-correlation learning architecture
Advances in neural information processing systems 2
Some new results on neural network approximation
Neural Networks
Towards designing artificial neural networks by evolution
Applied Mathematics and Computation - Special issue on articficial life and robotics
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
Engineering Applications of Artificial Intelligence
The scope of artificial neural network metamodels for precision casting process planning
Robotics and Computer-Integrated Manufacturing
An artificial neural network (p,d,q) model for timeseries forecasting
Expert Systems with Applications: An International Journal
ANN-based GA for generating the sizing curve of stand-alone photovoltaic systems
Advances in Engineering Software
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
A Novel Pruning Algorithm for Optimizing Feedforward Neural Network of Classification Problems
Neural Processing Letters
A new class of hybrid models for time series forecasting
Expert Systems with Applications: An International Journal
A hybrid neural network model based reinforcement learning agent
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Prediction of time-dependent structural behaviour with recurrent neural networks for fuzzy data
Computers and Structures
Hybridization of the probabilistic neural networks with feed-forward neural networks for forecasting
Engineering Applications of Artificial Intelligence
A hybrid algorithm for artificial neural network training
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Modeling plasma surface modification of textile fabrics using artificial neural networks
Engineering Applications of Artificial Intelligence
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Despite the fact that feedforward artificial neural networks (ANNs) have been a hot topic of research for many years there still are certain issues regarding the development of an ANN model, resulting in a lack of absolute guarantee that the model will perform well for the problem at hand. The multitude of different approaches that have been adopted in order to deal with this problem have investigated all aspects of the ANN modelling procedure, from training data collection and pre/post-processing to elaborate training schemes and algorithms. Increased attention is especially directed to proposing a systematic way to establish an appropriate architecture in contrast to the current common practice that calls for a repetitive trial-and-error process, which is time-consuming and produces uncertain results. This paper proposes such a methodology for determining the best architecture and is based on the use of a genetic algorithm (GA) and the development of novel criteria that quantify an ANN's performance (both training and generalization) as well as its complexity. This approach is implemented in software and tested based on experimental data capturing workpiece elastic deflection in turning. The intention is to present simultaneously the approach's theoretical background and its practical application in real-life engineering problems. Results show that the approach performs better than a human expert, at the same time offering many advantages in comparison to similar approaches found in literature.