Abstract
The artificial neural networks (ANN) are very often applied in many areas of toxicology for the solving of complex problems, such as the prediction of chemical compound properties and quantitative structure-activity relationship. The aim of this contribution is to give the basic knowledge about conception of ANN, theirs division and finally, the typical application of ANN will be discussed. Due to the diversity of architectures and adaptation algorithms, the ANNs are used in the broad spectrum of applications from the environmental processes modeling, through the optimization to quantitative structure-activity relationship (QSAR) methods. In addition, especially ANNs with Kohonen learning are very effective classification tool. The ANNs are mostly applied in cases, where the commonly used methods does not work.
Highlights
The term “artificial neural network” (ANN) originates from its biological pattern – neural network (NN), which represents the network of interconnected neurons in living organisms
The artificial neural networks (ANN) are based on the same conception as the biological ones, they are considered as the collection of interconnected computing units called artificial neurons (AN)
A three layer feed-forward ANN with back-propagation (BP) adaptation algorithm was used for quantitative structure-activity relationship (QSAR) investigation of organophosphorus compounds toxicity measured against midge larvae (Chironomus riparius) under varying temperature (11, 18, and 25 °C) and pH (6, 7, and 8) conditions and with or without sediment[9]
Summary
The artificial neural networks (ANN) are very often applied in many areas of toxicology for the solving of complex problems, such as the prediction of chemical compound properties and quantitative structure-activity relationship. The aim of this contribution is to give the basic knowledge about conception of ANN, theirs division and the typical application of ANN will be discussed. Due to the diversity of architectures and adaptation algorithms, the ANNs are used in the broad spectrum of applications from the environmental processes modeling, through the optimization to quantitative structure-activity relationship (QSAR) methods. The ANNs are mostly applied in cases, where the commonly used methods does not work
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