Abstract
New experimental data discussed in [5] are used in the present paper. Application of the penalized error function, Principle Data Analysis and Bayesian criterion of Maximum Marginal Likelihood enabled design and training of numerically efficient small neural networks. They were applied for identification of two compaction characteristics, i.e. Optimum Water Content and Maximum Dry Density of granular soils. © 2010 Springer-Verlag Berlin Heidelberg.
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CITATION STYLE
Kłos, M., & Waszczyszyn, Z. (2010). Prediction of compaction characteristics of granular soils by neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6352 LNCS, pp. 42–45). https://doi.org/10.1007/978-3-642-15819-3_5
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