Neuro-Fuzzy Knowledge Representation for Toxicity Prediction of Organic Compounds
نویسندگان
چکیده
Models based on neural and neuro-fuzzy structures are developed to represent knowledge about a large data set containing chemical descriptors of organic compounds, commonly used in industrial processes. The neuro-fuzzy models here proposed include both, QSARs and original numerical values. The developed approaches use various techniques to insert knowledge by training, and to map rules in neuro-fuzzy structures. These possibilities are evaluated and we show that the combination of neuro-fuzzy models, and strategies to insert data in the developed connectionist structures, improve over individual models for toxicity prediction.
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