Capability enhancement of probabilistic neural network for the design of breakwater armor blocks

  • Authors:

  • Doo Kie Kim;Dong Hyawn Kim;Seong Kyu Chang;Sang Kil Chang

  • Affiliations:

  • Department of Civil and Environmental Engineering, Kunsan National University, Kunsan, Jeonbuk, South Korea;Department of Ocean System Engineering, Kunsan National University, Kunsan, Jeonbuk, South Korea;Department of Civil and Environmental Engineering, Kunsan National University, Kunsan, Jeonbuk, South Korea;Department of Civil and Environmental Engineering, Kunsan National University, Kunsan, Jeonbuk, South Korea

  • Venue:
  • PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this study, the capability of probabilistic neural network (PNN) is enhanced. The proposed PNN is capable of reflecting the global probability density function (PDF) by summing the heterogeneous local PDF automatically determined in the individual standard deviation of variables. The present PNN is applied to predict the stability number of armor blocks of breakwaters using the experimental data of van der Meer, and the estimated results of PNN are compared with those of empirical formula and previous artificial neural network (ANN) model. The PNN showed better results in predicting the stability number of armor blocks of breakwaters and provided the promising probabilistic viewpoints by using the individual standard deviation in a variable.