Pile Samples Selection Method Based on Self-Organizing Maps Neural Network

Abstract:

One-dimensional self-organizing maps neural network (SOM) is used in pile samples selection, and the outcome can improve the accuracy of back-propagation neural network (BP) is proved. Firstly, 71 pile samples are divided into 5 groups according to SOM node weights. Each group is divided into training set, testing set, validation set to build 5 independent BP networks, called BP2. Secondly, 5 groups training set are merged into a new training set, similarly, a new test set and validation set to build another BP network, called BP1. At last, comparison of the performance of BP1 and BP2 show that using SOM network to select pile samples can build a BP network with better performance.

• Periodical: Applied Mechanics and Materials (Volumes 204-208)
• Pages: 41-44
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.204-208.41
• Online since: October 2012
• Authors: Si Si Liu*, Yun Lei Fan
• Keywords: Back Propagation Algorithm (BPA), Neural Network (NN), Pile Foundation, Sample Selection, Self-Organizing Maps

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