Evolving and clustering fuzzy decision tree for financial time series data forecasting
Expert Systems with Applications: An International Journal
Combined input variable selection and model complexity control for nonlinear regression
Pattern Recognition Letters
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
PSO based neural network for time series forecasting
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Effects of widely separated clusters on lotto-type competitive learning with particle swarm features
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Evolutionarily optimized features in functional link neural network for classification
Expert Systems with Applications: An International Journal
Methodological triangulation using neural networks for business research
Advances in Artificial Neural Systems
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Artificial neural networks (ANNs) have gained extensive popularity in recent years. Research activities are considerable, and the literature is growing. Yet, there is a large amount of concern on the appropriate use of neural networks in published research. The purposes of this paper are to: 1) point out common pitfalls and misuses in the neural network research; 2) draw attention to relevant literature on important issues; and 3) suggest possible remedies and guidelines for practical applications. The main message we aim to deliver is that great care must be taken in using ANNs for research and data analysis