An advanced probabilistic neural network for the design of breakwater armor blocks SCIE SCOPUS

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Title
An advanced probabilistic neural network for the design of breakwater armor blocks
Author(s)
Kim, Dookie; Kim, Dong Hyawn; Chang, Seongkyu; Yoon, Gil Lim
KIOST Author(s)
Yoon, Gil Lim(윤길림)
Publication Year
2007
Abstract
In this study, an advanced probabilistic neural network (APNN) method is proposed to reflect the global probability density function (PDF) by summing up the heterogeneous local PDF which is automatically determined in the individual standard deviation of variables. The APNN is applied to predict the stability number of armor blocks of breakwaters using the experimental data of van den Meer, and the estimated results of the APNN are compared with those of an empirical formula and a previous artificial neural network (ANN) model. The APNN shows better results in predicting the stability number of armor blocks of breakwater and it provided the promising probabilistic viewpoints by using the individual standard deviation in a variable.
ISSN
0890-5487
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/4787
Bibliographic Citation
CHINA OCEAN ENGINEERING, v.21, no.4, pp.597 - 610, 2007
Publisher
CHINA OCEAN PRESS
Subject
RUBBLE-MOUND BREAKWATERS; PREDICTION; DENSITY
Keywords
breakwater; armor block; stability number; multivariate gaussian distribution; classigication; artificial neural network (ANN); advanced probabilistic neural network (APNN)
Type
Article
Language
English
Document Type
Article
Publisher
CHINA OCEAN PRESS
Related Researcher
Research Interests

marine geotechnics,Dredging & Soft Ground,Offshore Wind Foundations,해양지반공학,준설 및 연약지반,해상풍력기초 및 신뢰성설계

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