Neural network knowledge extraction

More elaborate scheme.s can he envisaged. Thus, a. self-organizing neural network as obtained by the classification of a set of chemical reactions as outlined in Section 3,5 can be interfaced with the EROS system to select the reaction that acmaliy occurs from among various reaction alternatives. In this way, knowledge extracted from rcaetion databases can be interfaced with a reaction prediction system,... 

M. Holena, M. Baerns, Feedforward neural networks in catalysis, a tool for the approximation of the dependency of yield on catalyst composition, and for knowledge extraction, Catal. Today, 81 (2003), 485-494. 

While most combinatorial researches reported up to now involve the use of GA, using the traditional crossover and mutation operators (e.g. WGS 1), it has also been proposed to design new operators for each specific application, to improve search efficiency by means of knowledge extraction [32]. Hence, new methods that combine ES with a knowledge extraction engine have been reported recently within the field of heterogeneous catalysis, such as mining association rules [12, 18, 30, 33] and neural networks [19, 29, 34]. 

Holena, M., Baerns, M., Feedforward neural networks in catalysis, a tool for the approximation of the dependency of yield on catalyst composition, and for knowledge extraction, Catal. Today 2003, 81, 485-494. Serra, J. M., Corma, A., Argente, E., Valero, S., Botti, V., Neuronal networks for modeling of kinetic reaction data applicable to catalyst scale up and process control and optimization in the frame of combinatorial catalysis, Appl. Catal. A 2003, 254, 133-145. 

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