Artificial neural networks component analysis input

One commercially available sensor array analysis system, offered by Mosaic Industries [51], is Rhino , a microprocessor-based instrument with an array composed of discrete, resistive gas sensors. An artificial neural network processes sensor inputs and relates them to patterns established by training the instrument with gas components and mixtures of interest for a specific application. In principle, each system is customized for an application by the choice of sensors and the gas detection needs. Potential applications for this system are limited by the availability of suitable sensors and the complexity needed for discrimination. 

Artificial Neural Networks Based on Principal Component Analysis Input Selection for Quantification in Overlapped Capillary Electrophoresis Peaks. 

Fig. 5 a, b. Illustration of the computation principles for (a) artificial neural networks with input signals (array signals), hidden nodes chosen during the training of the net, and output signals (the parameters to be predicted) and (b) principal component analysis with two principal components (PC 1 and PC 2) based on three sensor signals (represented by the x, y and z axes). Normally reduces from approximately 100 signals down to two or three PCs... 

CLINICAL PATTERN RECOGNITION ANALYSIS APPLYING ARTIFICIAL NEURAL NETWORKS BASED ON PRINCIPAL COMPONENT ANALYSIS INPUT SELECTION... 

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