Next Article in Journal
The Influence of Effective Microorganisms on Microbes and Nutrients in Kiwifruit Planting Soil
Next Article in Special Issue
Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection
Previous Article in Journal
Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies
Previous Article in Special Issue
Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine
Article Menu

Export Article

Open AccessArticle
Appl. Sci. 2016, 6(6), 164; doi:10.3390/app6060164

Determination of Optimal Initial Weights of an Artificial Neural Network by Using the Harmony Search Algorithm: Application to Breakwater Armor Stones

1
Department of Civil and Environmental Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
2
Department of Energy and Information Technology, Gachon University, 1342 Seongnamdae-ro, Sujeong-gu, Seongnam-si, Gyeonggi-do 13120, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Christian Dawson
Received: 31 March 2016 / Revised: 15 May 2016 / Accepted: 16 May 2016 / Published: 31 May 2016
(This article belongs to the Special Issue Applied Artificial Neural Network)
View Full-Text   |   Download PDF [2627 KB, uploaded 31 May 2016]   |  

Abstract

In this study, an artificial neural network (ANN) model is developed to predict the stability number of breakwater armor stones based on the experimental data reported by Van der Meer in 1988. The harmony search (HS) algorithm is used to determine the near-global optimal initial weights in the training of the model. The stratified sampling is used to sample the training data. A total of 25 HS-ANN hybrid models are tested with different combinations of HS algorithm parameters. The HS-ANN models are compared with the conventional ANN model, which uses a Monte Carlo simulation to determine the initial weights. Each model is run 50 times and the statistical analyses are conducted for the model results. The present models using stratified sampling are shown to be more accurate than those of previous studies. The statistical analyses for the model results show that the HS-ANN model with proper values of HS algorithm parameters can give much better and more stable prediction than the conventional ANN model. View Full-Text
Keywords: armor stones; artificial neural network; harmony search algorithm; rubble mound structure; stability number armor stones; artificial neural network; harmony search algorithm; rubble mound structure; stability number
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Lee, A.; Geem, Z.W.; Suh, K.-D. Determination of Optimal Initial Weights of an Artificial Neural Network by Using the Harmony Search Algorithm: Application to Breakwater Armor Stones. Appl. Sci. 2016, 6, 164.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top