Multilayer preceptron

When a solution containing variable amounts of malate and tartrate was added to the multicomponent sensor, a characteristic UV-Vis spectrum response was obtained. Since the different receptor-dye complexes and the free dyes all have different colors, the information about the analytes was dispersed over the entire spectrum. To analyze the spectral changes, a multilayer preceptron (MLP) artificial neural network (ANN) was employed (see Glossary in Box 7.1). To train the ANN, the UV-Vis absorption data of 45 calibration samples containing different amounts of malate and tartrate (0-1.2 mM) were used. For each sample, the absorbances at... 

The structure of ANNs is reminiscent of biological neural networks. Several simple nodes ( neurons ) are connected to form a network of nodes. Algorithms define the strengths between the neurons. Multilayer preceptron (MLP) is one of the most popular and traditional models of ANNs [15]. Its structure consists of an input layer, one or more hidden layers, and an output layer. All layers contain a variable number of neurons. The network maps the input data (e.g., UV-Vis data) to a set of outputs (e.g., concentrations). A training data set can be used to optimize the strength of the connections. The trained network is then able to make predictions for a validation data set. 

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See also in sourсe #XX -- [ , ]

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