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Cluster Sampling Method

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

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Related Experiment Video

Updated: Oct 8, 2025

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RBFNN Design Based on Modified Nearest Neighbor Clustering Algorithm for Path Tracking Control.

Dongxi Zheng^1,2, Wonsuk Jung³, Sunghoon Kim^1,4

¹Department of Electronics Convergence Engineering, Wonkwang University, Iksan 54538, Korea.

Sensors (Basel, Switzerland)

|December 28, 2021

Summary

This study introduces a novel clustering algorithm for training radial basis function neural networks (RBFNNs). This method enhances accuracy and simplifies RBFNNs by optimizing basis functions without complex parameter tuning.

Keywords:

nearest neighbor-based clustering (MNNC)path tracking radial basis function neural network (RBFNN)sample optimization

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Area of Science:

Artificial Intelligence
Machine Learning
Neural Networks

Background:

Radial basis function neural networks (RBFNNs) are crucial for complex tasks.
Optimizing RBFNNs requires careful selection of basis function parameters.
Supervised learning methods for parameter optimization increase system complexity.

Purpose of the Study:

To propose a modified nearest neighbor-based clustering algorithm for training RBFNNs.
To simplify RBFNN training by reducing parameter determination.
To improve the accuracy and efficiency of RBFNNs.

Main Methods:

A modified nearest neighbor-based clustering algorithm is developed.
The algorithm adapts to varying data densities and minimizes computational load.
It optimizes basis function centers and numbers without requiring empirical parameter setting.

Main Results:

The clustering algorithm effectively groups data samples and identifies outliers.
RBFNNs trained with this method show improved curve fitting accuracy compared to conventional approaches.
Path tracking control for magnetic microrobots using the proposed RBFNN demonstrated effectiveness.

Conclusions:

The modified nearest neighbor-based clustering algorithm offers an efficient and accurate method for RBFNN training.
This approach enhances RBFNN performance in curve fitting and control applications.
Significant improvements in test and training accuracy (23.5% and 7.5%) were achieved.