A pattern recognition approach to understanding the multi-layer perceptron
Pattern Recognition Letters
Multilayer feedforward networks are universal approximators
Neural Networks
What size net gives valid generalization?
Neural Computation
Creating artificial neural networks that generalize
Neural Networks
Neural networks in C++: an object-oriented framework for building connectionist systems
Neural networks in C++: an object-oriented framework for building connectionist systems
The computational brain
Artificial Neural Networks: A Tutorial
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
Neural Networks
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Logistic regression and artificial neural network classification models: a methodology review
Journal of Biomedical Informatics
Regression Modeling Strategies
Regression Modeling Strategies
A comparison of some error estimates for neural network models
Neural Computation
Backpropagation neural nets with one and two hidden layers
IEEE Transactions on Neural Networks
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In this letter, we use the firing rates from an array of olfactory sensory neurons OSNs of the fruit fly, Drosophila melanogaster, to train an artificial neural network ANN to distinguish different chemical classes of volatile odorants. Bootstrapping is implemented for the optimized networks, providing an accurate estimate of a network's predicted values. Initially a simple linear predictor was used to assess the complexity of the data and was found to provide low prediction performance. A nonlinear ANN in the form of a single multilayer perceptron MLP was also used, providing a significant increase in prediction performance. The effect of the number of hidden layers and hidden neurons of the MLP was investigated and found to be effective in enhancing network performance with both a single and a double hidden layer investigated separately. A hybrid array of MLPs was investigated and compared against the single MLP architecture. The hybrid MLPs were found to classify all vectors of the validation set, presenting the highest degree of prediction accuracy. Adjustment of the number of hidden neurons was investigated, providing further performance gain. In addition, noise injection was investigated, proving successful for certain network designs. It was found that the best-performing MLP was that of the double-hidden-layer hybrid MLP network without the use of noise injection. Furthermore, the level of performance was examined when different numbers of OSNs used were varied from the maximum of 24 to only 5 OSNs. Finally, the ideal OSNs were identified that optimized network performance. The results obtained from this study provide strong evidence of the usefulness of ANNs in the field of olfaction for the future realization of a signal processing back end for an artificial olfactory biosensor.