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An artificial neural network was applied to slag glass-ceramic complex data analysis. A three-layer feedforward network was built with a new robust learning algorithm based on a concept of  entire error modifying . These studies indicate that the network has an excellent learning ability when its topology is  M  –(2 M  + 1) – 1 and when an appropriate study error was chosen. Research shows that this slag glass-ceramic neural network is robust, accurate and stable in complex data analysis, and can disclose the relationship of elemental compositions, processing parameters and material properties of slag glass-ceramic effectively.

Model of artificial neural network for complex data analysis in slag glass-ceramic