Abstract
A cascade correlation learning architecture has been devised for the first time for radial basis function processing units. The proposed algorithm was evaluated with two synthetic data sets and two chemical data sets by comparison with six other standard classifiers. The ability to detect a novel class and an imbalanced class were demonstrated with synthetic data. In the chemical data sets, the growth regions of Italian olive oils were identified by their fatty acid profiles; mass spectra of polychlorobiphenyl compounds were classified by chlorine number. The prediction results by bootstrap Latin partition indicate that the proposed neural network is useful for pattern recognition.
Highlights
Artificial neural networks (ANNs) are widely used pattern recognition tools in chemometrics
The back-propagation neural network (BNN) model gave an open, sigmoidal shaped response surface that divides the output space into regions that correspond to the four classes
When the BNN model extrapolates outside the region defined by the data objects, the response can be larger than unity, which occurs when the output units are linear
Summary
Artificial neural networks (ANNs) are widely used pattern recognition tools in chemometrics. The most commonly used neural network for chemists is the back-propagation neural network (BNN). The. BNN is a feed forward neural network, usually trained by error back-propagation [1, 2]. BNNs have been applied to a broad range of chemical applications. Recent analytical applications of BNNs in fields such as differential mobility spectrometry [3] and near infrared spectroscopy [4] have been reported in the literature. BNNs have been proven a useful type of ANNs in chemometrics. BNNs converge slowly during training especially when the network contains many hidden neurons.
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