Modified Probabilistic Neural Network Considering Heterogeneous Probabilistic Density Functions in the Design of Breakwater

In this study, a modified probabilistic neural network approach is proposed. The global probability density function (PDF) of variables is reflected by summing the heterogeneous local PDFs automatically determined in the individual standard derivation of each variable. The proposed modified probabil...

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Bibliographic Details
Published inKSCE journal of civil engineering Vol. 11; no. 2; pp. 65 - 71
Main Authors Kim, Doo Kie, Kim, Dong Hyawn, Chang, Seong Kyu, Chang, Sang Kil
Format Journal Article
LanguageEnglish
Published Seoul 대한토목학회 01.03.2007
Springer Nature B.V
Subjects
Armor
Breakwaters
Mathematical analysis
Neural networks
Probability density functions
Probability theory
Stability
Statistical analysis
Studies
classification
modified probabilistic neural network (MPNN)
armor block
stability number multivariate Gaussian distribution
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Summary:In this study, a modified probabilistic neural network approach is proposed. The global probability density function (PDF) of variables is reflected by summing the heterogeneous local PDFs automatically determined in the individual standard derivation of each variable. The proposed modified probabilistic neural network (MPNN) is applied to predict the stability number of armor bøcks of break waters using the experiental data of van der Meer, and the estimated results of the MPNN are compared with those of conventional probabilistic neural network. The MPNN shows improved results in predicting the stability number of armor blocks of breakwaters and in providing the promising reliability for stability numbers estimated by using the individual standard deviation in a variable.
ISSN:1226-7988
1976-3808
DOI:10.1007/BF02823849