A machine learning approach to the estimation of the liquidus temperature of glass-forming oxide blends
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[1] Kurt Hornik,et al. Degree of Approximation Results for Feedforward Networks Approximating Unknown Mappings and Their Derivatives , 1994, Neural Computation.
[2] Kenneth J. Hunt,et al. Neural Adaptive Control Technology , 1996 .
[3] A. Priven. Calculation of the properties of oxide glasses and melts from composition : Problems and prospects , 1998 .
[4] R. Cahn,et al. Glasses and amorphous materials , 1991 .
[5] Christopher M. Bishop,et al. Neural networks for pattern recognition , 1995 .
[6] William H. Press,et al. Numerical recipes in FORTRAN (2nd ed.): the art of scientific computing , 1992 .
[7] R. Backman,et al. Model for liquidus temperature of multi-component silicate glasses , 1997 .
[8] Sung S. Kim,et al. Thermodynamic Modeling of Phase Diagrams in Binary Alkali Silicate Systems , 1991 .
[9] C. L. Babcock. Silicate Glass Technology Methods , 1988 .
[10] H. G. Suzuki,et al. Precipitation characteristics of Cu–15Cr–0·15Zr in situ composite , 2000 .
[11] Gérard Dreyfus,et al. Local Overfitting Control via Leverages , 2002, Neural Computation.
[12] G. Piepel,et al. Liquidus temperatures of HLW glasses with zirconium-containing primary crystalline phases , 1997 .